Adv DB: Data Warehouse & Data Mining

Data warehouses allow for people with decision power to locate the adequate data quickly from one location that spans across multiple functional departments and is very well integrated to produce reports and in-depth analysis to make effective decisions (MUSE, 2015a). The data warehouse doesn’t solve the: Who, What, Where, When, Why and How, but that is where data mining can help.  Data warehouse, when combined with data mining tools, can create a decision support system (DSS), which can be used to uncover/discover hidden relationships within the data (MUSE, 2015b). DSS needs both a place to store data and a way to sort meaningful data in order to make sense of the data and provide meaningful insights to the decision-maker.  Data that can be used for meaningful insights must be prepared/transformed (and checked for quality) while in the data warehouse, but must be completed before the data is used in a data mining tool.  Also, results from the data mining tool can be placed back into the data warehouse to allow its results to be seen by all end-users and to be reused by others.

Data Warehouse & Data Mining

A data warehouse is a centralized collection of data that is consistent, subject-oriented, integrated, special variant and/or temporally variant, nonvolatile data to enable decisions makers to make desirable business decisions based on their gathered insights and predictions from the data about the near future (Tryfona et al, 1999). Ballou & Tayi (1999) stated that a key feature of a data warehouse is its usage for decision making not for operational purposes.  Nevertheless, data warehouses don’t solve the questions: Who, What, When, Where, Why and How, it’s just a data depository (MUSE, 2015b). Hence, it validates what Tryfona et al (1999) stated, there is little distinction/differentiator on how data is modeled in a data warehouse as with a database. Databases though can and are used in operational situations, thus invalidating Tryfona et al (1999) argument, because as Ballou & Tayi (1999) pointed out operational data usually focuses heavily on current data whereas decision-makers look at historical data across time intervals to make temporal comparisons.

Databases and/or data warehouses cannot make a decision all on its own, but they are the platform to which data is stored centrally so that the right decision analysis techniques can be conducted on the data in order to provide meaning from them. The right decision analysis technique comes from data mining, which helps find meaningful once-hidden patterns from the data (in this case stored in the data warehouse).  Data mining can look into the past and current data to make predictions into the future (Silltow, 2006).   Though this is nothing new, statisticians have been using these techniques in a manual fashion for years to help discover knowledge from data. Thus, discovering knowledge through these centrally stored data, which can possibly come from multiple sources in a business or other data creation system that could be tied/linked together is what a warehouse does best (Connolly & Begg, 2015). What data warehouses also enable is using the same data in new ways to discover new insights about a subject than what the original purpose was (reuse) for collecting that data (Ballou & Tayi, 1999).  Data warehouses can support several low-level organizational decisions as well as high-level organizational (enterprise-wide) decisions.  Suitable applications to feed data into a data warehouse to aid in decision making can come from: mainframes, proprietary file systems, servers, internal workstations, external website data, etc.  Storing some data offline or online helps mainly to improve querying speeds. Summarized data, which is updated automatically as new data enters the warehouse, can help improve query speeds, while detailed data can be stored online if it can help support/supplement summarized data (Connolly & Begg, 2015).

Failure in the implementation of a data warehouse can be generated from poor data quality. Data quality should be built into the data warehouse: planning, implementation, and maintenance phases.  Ballou & Tayi (1999) warned that even though this feature of data stored in a data warehouse is a key driver for companies to adopt a warehouse is that data quality must be preserved.  Data quality encompasses the following attributes: accuracy, completeness, consistency, timeliness, interpretability, believability, value-added, and accessibility.  Most people generating data are familiar with its error rates, margins of error, its deficiencies, and idiosyncrasies, but when rolled up in a data warehouse (and it is not communicated properly), people outside of that data-generating organization will not know this and their final decisions could be prone to errors.  One must consider the different needs for data quality within a data warehouse, as the levels of quality needed for relevant decision making, project design, future needs, etc.  We must ask from our data providers what is unsatisfactory and to what quantifiable level is the current data that they are providing into the data warehouse (Ballou & Tayi, 1999).  As the old adage goes “Garbage In – Garbage Out”.

So, what can cause data quality issues?  Let’s take a mortgage company, REMAX, which has a data warehouse, however, the data for sales isn’t consistent, because there are different definitions of what a sale/price could be based on differing stakeholders.  The mortgage company can say that a sale is the closing price of the house, whereas REMAX may say the negotiated list price of house, the broker may say the final settlement price of the house after the home inspection, the insurance company is the price of the building materials in the house plus 65-70 thousand dollars for internal possessions.  This may be all the data that REMAX wants to have to provide the best service to their customer and to provide a realistic view of what goes on in purchasing a house, monetarily, but REMAX must know this information ahead of time as they input this data into their data warehouse.  This could be valuable information for the home buyer when they are deciding which one of two to three properties that they would like own.  There could be syntactic inconsistencies between all these sources of data like $60K, $60,000, $60,000.00, 60K, $60000, etc.

Another way the implementation of a data warehouse could fail, according to Ballou & Tayi (1999), can come from not including appropriate data (in other words: data availability).   Even though critical data can exist among: soft data (uncertain data), text-based data, external sources of data, this set of data could altogether be ignored.  They continue to add that this type of data, so long as it can support the organization in any “meaningful way” should be added into the centralized data warehouse.  Though one must weigh the high cost of acquiring the data that may be useless because it is relatively easy (inexpensive) to delete data that is rarely used once in the system.  But, then there is an opportunity cost to adding irrelevant data, we could have used our resources to improve the timeliness of the current data (or provide real-time data) or eliminating null values in a different data set that is already in the system.

To solve the issue of data quality, decision-makers and data warehouse managers must think systematically about what data is required, why it is required, and how should it be collected and used (Ballou & Tayi, 1999).  This could be done when a data warehouse manager asks the end-users what decisions this data warehouse will support.  From that information one can decipher what is required from these stakeholders through the MoSCoW: What is a “Must have”?; What is a “Should have”?; What is a “Could have?”; and What is a “Wish to have”? In the REMAX case, they should have the final asking price before the inspection listed (as they do) as a “Must have”, typical closing costs for a house in that price range that is provided by the mortgage company as a “Should have”, the average house insurance costs as a “Could Have”, etc. Balou & Tayi (1999) said that other factors can affect data quality enhancement projects, like the: Current quality, required quality, anticipated quality, priority of organizational activity (as aforementioned with MoSCoW), Cost of data quality enhancements (and their aforementioned tradeoffs/opportunity costs), and their value-added to the data warehouse.  Data quality is needed in order to use data mining tools, and many papers using data mining or text mining always talk about a preprocessing step that must occur before full analysis can begin: Nassirtoussi et al (2015),  Kim et al (2014), Barak & Modarres (2015), etc.

According to Silltow (2006), data mining tools can be group into three types: Traditional (have complex algorithms and techniques to find hidden patterns in the data and highlight trends), dashboard (data changes are shown on a screen which is mostly used to monitor information), and text-mining (using complex algorithms and techniques to find hidden patterns in text data, even to a point of figuring out the sentiment in a string of words and can include video and audio data).  These data mining techniques range from artificial neural networks (prediction models that use training data to learn and then make forecasts) like in Nassirtoussi et al (2015) and Kim et al (2014); decision trees (uses a bunch of defined if-then statements, also known as rules, and are easier to understand the results of the data) like in Barak & Modarres (2015); nearest neighbor (uses similar past data to make predictions into the future), etc.

Finally, another aspect of data quality is the output of the data from data mining tools, especially since we can then plug the output back into the data warehouse for future reuse.  Data mining tools are just that, automatic algorithms used to discover knowledge.  These tools lack the intuitive nature presented in humans to decipher between a relevant and irrelevant correlation.  For instance, data stored in a hospital data warehouse may link data collected in the summer of insane amount of increased ice cream consumption which could lead to obesity and the number of pool/beach drownings and say that ice cream consumption leads to them, rather than looking at the fact that they both occur in the summer but are not necessarily causing one or the other.  This is why Silltow (2006) suggest that all results provided by these tools be quality checked after utilized to not give out false, irrelevant insights that are preposterous when analyzed by a human.


Data warehouses allow for people with decision power to locate adequate data quickly to make effective decisions. The data that is planned, entered, maintained should be of acceptable quality.  Poor quality in the data may drive poor quality decisions.  The best way to improve data quality is by looking at the eight factors of data quality as aforementioned when asking stakeholders what data from a systemic point of view would be useful in a data warehouse.  Sometimes asking what data should be included is very hard for decision-makers to make at that moment, though they could have a general idea of what decisions they need to make soon.  Data collection and quality must be weighed against all of their cost and their significance.


  • Ballou, D. P., & Tayi, G. K. (1999). Enhancing data quality in data warehouse environments. Communications of the ACM, 42(1), 73-78.
  • Barak, S., & Modarres, M. (2015). Developing an approach to evaluate stocks by forecasting effective features with data mining methods. Expert Systems with Applications, 42(3), 1325–1339.
  • Connolly, T. & Begg, C. (2015).  Database Systems:  A Practical Approach to Design, Implementation, and Management, Sixth Edition.  Boston:  Pearson.
  • Kim, Y., Jeong, S. R., & Ghani, I. (2014). Text opinion mining to analyze news for stock market prediction. Int. J. Advance. Soft Comput. Appl, 6(1).
  • My Unique Student Experience (2015a). Data Warehousing Concepts and Design. Retrieved from: Asset.aspx?MID=1819502&aid=1819506
  • My Unique Student Experience (2015b). Online Analytical Processing. Retrieved from:
  • Nassirtoussi, A. K., Aghabozorgi, S., Wah, T. Y., & Ngo, D. C. L. (2015). Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment. Expert Systems with Applications, 42(1), 306-324.
  • Silltow, J. (2006) Data mining 101: Tools and techniques.  Retrieved from:
  • Tryfona, N., Busborg, F., & Borch Christiansen, J. G. (1999, November). starER: a conceptual model for data warehouse design. In Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP (pp. 3-8). ACM.

Adv DB: Conducting data migration to NoSQL databases

Relational databases schema design (primarily ERDs) are all about creating models, then translating it a schema to which is normalized, but one must be an oracle to anticipate a holistic, end-to-end design, or else suffer when making changes to the database (Scherzinger et al, 2013).  Relational databases are poor at data replication, horizontal scalability, and high availability rates (Schram & Anderson, 2012).  Thus, waterfall approaches to database design are no longer advantageous, and like software development databases can be designed with an agile mentality.  Especially as data store requirements are always evolving. Databases that adopt a “Schema-less” (where data can be stored without any predefined schema) or an “Implicit Schema” (where the data definition van be taken from a database from an application in order to place the data into the database) in “Not Only SQL” (NoSQL) can allow for agile development on a release cycle that can vary from yearly, monthly, weekly, or daily, which is completely dependent on the developers’ iteration cycle (Sadalage & Fowler, 2012).  Taking a look at a blogging agile development lifecycle (below) can show how great schema-less or implicit schemas in NoSQL database development can become, as well as the technical debt that is created, which can cause migration issues down the line.


We start a blogging site called “” and we are in an agile environment, which means iterative improvements and each iteration produces a releasable product (even if we decide not to make a release or update at the end of the iteration).  As a programming team, they have decided that the minimum viable product will consist of the fields, title, and content for the blogger and comments from other people.  This is a similar example proposed by Scherzinger et al in 2013, as they try to explain how implicit schemas work.  In the second iteration, the programming team for “” has discovered an abuse on the commenting section of the blog.  People have been “trolling” the blog, thus to mitigate this, they implemented a sign-in process with a username and password that is taken from Facebook, which allows for liking a post as well.  Rather than having bloggers to recreate their content, the programmers make the implementation of this update for current and future posts. In a third iteration, the programming teams to institute a uniformed nomenclature to some of their fields.  Rather than changing all the posts from the first two iterations, the programmers decide to enforce these changes moving forward.

Now, one can see how useful a schema-less development (provided by NoSQL) can become.   There is no downtime to how the site interacts and adds no additional burden to the end-users when an update occurs. But, we now have to worry about migrating these three data classes (or as Scherzinger et al calls it technical debt), but what if a commenter goes and comments in a post made in iteration one or two after iteration three has been implemented, we may then have four to five different data classes.  These developers love to develop code and add new features rather than maintain code, which is why this form of developing a database is great, but as we can see technical debt can pile on quickly.  Our goal is to manage a schema of this data, yet have the flexibility of a schema-less database system.

Types of Migration

The migration of data in and out of a data store is usually enabled through a replication scheme (Shirazi et al, 2012) conducted through an application.  There are two primary types of data migration per Scherzinger et al (2013): eager and lazy.  Eager migration means we migrate all the data in a batched fashion, one-by-one retrieval from the data store, transform it and write it back into the data store.  As data becomes larger, eager migration can become resource-intensive and could be a wasted effort. Wasted efforts can come from stale data.  Thus, the lazy approach is considered as a viable option.  Transformations are conducted when a piece of data is touched, so only live and hot data (relevant data) is updated.  Even though this approach saves on resources, if an entity becomes corrupted, there may be no way to retrieve it.  In order to do the migration, an application needs to create an “implicit-schema” on the “schema-less” data.

NoSQL and its multiple flavors

NoSQL databases can deal with aggregate data (relationships between units of data that can be relationally mapped), using key-value, document, and column friendly databases (Scherzinger et al, 2013, Sadalage & Fowler, 2012, Schram & Anderson, 2012).  There also exist graphical databases (Sadalage & Fowler, 2012).  Key-value databases deal with storing data with a unique key and value, while document databases store documents or their parts in a value. (Scherzinger et al, 2013). People can blur the line between this and key-value databases by placing an ID field, but for the most part, you will query a document database rather than look up a key or ID (Sadalage & Fowler, 2012). Whereas column friendly databases store the information in transposed table structures (as columns rather than rows).  Graph databases can show relationships with huge datasets that are highly interconnected, and the complexity of the data is emphasized in this database rather than the size of data (Shirazi et al, 2012).  A further example of a graphical database is shown in the health section in the following pages.  Migrations between the multiple flavors of NoSQL databases allow for one to exploit the strengths and mitigate the weakness between the types when it comes to analyzing the large data quickly.

Data Migration Considerations and Steps

Since data migration uses replication schemes from an application, one must consider how complex writing a SQL query would be if this were a relational database scheme (Shirazi et al, 2012).  This has implications on how complex transforming data or migrating it would be under NoSQL databases, especially when big data is introduced into the equation.  Thus, the pattern of database design must be taken into account when migrating data between relational databases to NoSQL database, or between different NoSQL database types (or even provider). Also, each of these database types treats NULL values differently, some NoSQL databases don’t even waste the storage space and ignore NULL values, some systems have them as in relational databases, and some systems allow for it, but don’t query for it (Scherzinger et al, 2013).  Scherzinger et al (2013) suggest that when migrating data, data models (data stored in the databases that belong to a object or a group, which can have several properties) query models (data that can be inserted, transformed and deleted based on a key-value, or some other kind identification), and freedom from schema (the global structure of the data that can or cannot be fixed in advance) must be taken into account. Whereas, Schram & Anderson in 2012, stated that data models are key when making design changes (migrations) between database systems. Since in NoSQL data is “schema-less” there may not be any global structure, but applications (such as web user-interfaces) built on top of the data-stores can display an implicit structure, and from that, we can list a few steps to consider when migrating data (Tran et al, 2011):

  • Installation and configuration
    1. Set up development tools and environment
    2. Install and set up environments
    3. Install third-party tools
  • Code modification
    1. Set up database connections
    2. Database operation query (if using a NoSQL database)
    3. Any required modifications for compatibility issues
  • Migration
    1. Prepare the database for migration
    2. Migrate the local database to the NoSQL database (the schema-less part)
    3. Prepare system for migration
    4. Migrate the application (the implicit-schema part)
  • Test (how to ensure the data stored in the databases matched with the “Implicit Schema” embedded in the applications when the “Implicit Schema” has experienced a change)
    1. Test if the local system works with a database in NoSQL
    2. Test if the system works with databases in NoSQL
    3. Write test cases and test for functionality of the application in NoSQL

When doing code modification (step 2) from a relational database to a NoSQL database the more changes will be required, and JOIN operations may not be fully supported.  Thus, additional code may be required in order to maintain the serviceability of the application, pre-migration, during migration and post-migration (Tran et al, 2011).  Considering ITIL Service Transition standards, the best time to do a migration or update is in windows of minimum usage by end-users, while still maintaining agreed-upon minimum SLA standards.  As stated in Schram & Anderson (2012) they didn’t want their service to break while they were migrating their data from a relational database to a NoSQL column friendly database.  Other issues, like compatibility between the systems housing the databases or even database types, can also add complexity to migration.  When migrating (step 3) SQL scripts need to be transformed as well, to align with the new database structure, environment, etc. (Tran et al, 2011). Third-party apps can help to a degree with this.  If the planning phase was conducted correctly this phase should be relatively smooth.  Tran et al (2011) stated that there are at least 8 features that drive the cost of migration: (1) Project team’s capability, (2) Application/Database complexity, (3) Existing knowledge and experience, (4) Selecting the correct database and database management system, (5) Compatibility issues, database features, and (8) Connection issues during migration.


A database was created from 7.2M medical reports, in order to understand human diseases, called HealthTable.  The authors in Shirazi et al in 2012, decided to convert a column store into a graph database of Health Infoscape (Table 1 to Figure 1).  Each cause/symptom stems from disease (Dx), yet the power of graph databases as aforementioned are shown, thus facilitating data analysis, even though column friendly databases provide an easier way to maintain the 7.2M data records.

Table 1. HealthTable in Hbase per Shirazi et al (2012).

Row key Info Prevalence Causes
D1 Name Category Female Male Total Cause1 Cause2 Cause3
Heartburn Digestive system 9.4% 9% 9.2% D2    
1 1 1 1 1 2    
D2 Chest Pain Circulatory System 6.8% 6.8% 6.8%      
3 3 3 3 3      
D4 Dizziness Nervous System 4% 2.8% 3.5%      
5 5 5 5 5      

health graph

Figure 1. HeathGraph Bases on HealthTable


From these two use cases (Heath and Blogging) is that data migration can be quite complicated.  Schema-less databases allow for a more agile approach to developing, whereas the alternative is best for the waterfall.  However, with waterfall development slowly on the decay, one must also migrate to other forms of development.  Though applications/databases can migrate from relational databases to NoSQL and thus require a lot of coding because of compatibility issues, applications/databases can also migrate between different types of NoSQL databases.  Each database structure has its strengths and weakness, and migrating data between these databases can provide opportunities for knowledge discovery from the data that is contained within them.  Migrating between database systems and NoSQL types should be conducted if it fulfills many of the requirements and promises to reduce the cost of maintenance (Schram & Anderson, 2012).


  • Sadalage, P. J., Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, 1st Edition. [VitalSource Bookshelf Online]. Retrieved from
  • Scherzinger, S., Klettke, M., & Störl, U. (2013). Managing schema evolution in NoSQL data stores. arXiv preprint arXiv:1308.0514.
  • Schram, A., & Anderson, K. M. (2012). MySQL to NoSQL: data modeling challenges in supporting scalability. In Proceedings of the 3rd annual conference on Systems, programming, and applications: software for humanity (pp. 191-202). ACM.
  • Shirazi, M. N., Kuan, H. C., & Dolatabadi, H. (2012, June). Design Patterns to Enable Data Portability between Clouds’ Databases. In Computational Science and Its Applications (ICCSA), 2012 12th International Conference on (pp. 117-120). IEEE.
  • Tran, V., Keung, J., Liu, A., & Fekete, A. (2011, May). Application migration to cloud: a taxonomy of critical factors. In Proceedings of the 2nd international workshop on software engineering for cloud computing (pp. 22-28). ACM.

Adv DB: Data Services in the Cloud Service Platform

Rimal et al (2009), states that Cloud Computing Systems are a disruptive service that has gained momentum. What makes it disruptive is that it has similar properties of prior technology, while adding new features and capabilities (like big data processing) in a very cost-effective way.   It has become part of the XaaS (where X can be infrastructure, hardware, software, etc.) as a Service.  According to Connolly & Begg (2014), Data as a Service (DaaS) and Database as a Service (DBaaS) are considered as cloud-based solutions. DaaS doesn’t use SQL interfaces, but it does enable corporations to access data to analyze value streams that they own or those they can easily expand into. DBaaS must be continuously monitored and improved on, because they usually serve multiple organizations, with the added benefit of providing charge-back functions per organization (Connolly & Begg, 2014) or a pay-for-use model (Rimal et al, 2009).  However, one must pick a solution that best serves their business’/organization’s needs.

Benefits of the service

Connolly & Begg (2014), stated that there are benefits to cloud computing such as Cost-reduction due to lower CapEx, ability to scale up or down based on data demands, needs for higher security making data stored here more secure than in-house solutions, 24/7 reliability can be provided, faster development time because time is not take away from building the DBMS from scratch, finally sensitivity/load testing can be made readily available and cheaply because of lower CapEX.  Rimal et al (2009), stated that the benefits came from lower costs, improved flexibility and agility, and scalability.  You can set up systems as quickly as you wish, under a pay-as-you-go model and is great for disaster recovery efforts (as long as the disaster affected your systems, not theirs).   Other benefits could be seen from an article looking at the Data-as-a-Service in the health field: a low cost to implementation and maintainability of databases, defragmentation of data, exchange of patient data across the heady provider organization, and provide a mode for standardization of data types, forms, and frequency to capture data.  From a health-care perspective, it could lead to supporting research, strategic planning of medical decisions, improve data quality, reduce cost, reduce resource scarcity issues from an IT perspective, and finally provide better patient care (AbuKhousa et al, 2012).

Problems that can be removed because of the service

Unfortunately, there are two sides to a coin.  Given that on a cloud service there exists network dependency, such that if the supplier has a power outage, the consumer will not have access to their data.  Other network dependencies can occur like peak service hours, where service tends to be degraded compared to if the company used the supplier during its off-peak hours.  Quite a few organizations use Amazon EC2 (Rimal et al, 2009) as their Cloud DBMS, which if that system is hacked or security is breached the problem is bigger than if it were carried out to only one company. There are system dependencies, like in the case of disaster recovery (AbuKhousa et al, 2012), organizations are as strong as their weakest link when it comes to a disaster, if the point of failure in the service, and there are no other mitigation plans, that organization may have a hard time recuperating their losses.  Also, placing data into these services, you lose control over the data (lose control over availability, reliability, maintainability, integrity, confidentiality, intervenability, and isolation) (Connolly & Begg, 2014).  Rimal et al (2009) stated clear examples of outages that existed in Services like Microsoft (down 22 hours in 2008), or Google Gmail (2.5 hours in 2009), etc.  All of these lack of control points is perhaps one of the main reasons why certain government agencies have had a hard time adopting a commercialized cloud service provider, however they are attempting to create internal clouds.

Architectural view of the system

The overall cloud architecture is the layered system that serves as a single point of contact and uses software applications over the web, using an infrastructure, which draws on resources from necessary hardware to complete a task (Rimal et al, 2009).  Adopted from Rimal et al (2009) the figure below is what these authors describe as the layered cloud architecture.

Software-as-a-Service (SaaS)

Platform-as-a-Service (PaaS)

Developers implementing cloud applications

Infrastructure-as-a-Service (IaaS)

[(Virtualizations, Storage Networks) as-a-Service]

Hardware-as-a-Service (HaaS)

Figure 1. A layered cloud architecture.

Cloud DBMS can be private (an internal provider where the data is held within the organization), public (an external provider manages resources dynamically across their system and through multiple organizations supplying them data), and hybrid (consists of multiple internal and external providers) as defined in Rimal et al (2009).

A problem with DBaaS is the fact that databases between multiple organizations are stored by the same service provider.  Where data can be what separates one organization from its competitors, they must consider the following architecture: Separate Servers, Shared Server but different database processes, Shared databases but separate databases, Shared databases but separate schema, or Shared databases but shared schema (Connolly & Begg, 2014).

Let’s take two competitors: Walmart & Target.  Two supergiant organizations that have trillions of dollars and inventory flowing in and out of their systems monthly.  Let’s also assume three database tables with automated primary keys and foreign keys that connect the data together: Product, Purchasing, and Inventory.  Another set of assumptions: (1) their data can be stored by the same DBaaS provider, (2) their DBaaS provider is Amazon.

While Target and Walmart may use the same supplier for their DBaaS, they can select one of those five architectural solutions to avoid their valuable data to be seen.  If Walmart and Target purchased separate servers, their data can be safe.  They could also go this route if they want to store their huge data and/or have many users of their data. Now if we narrow down our assumptions to the children’s toy section (due to breaking up the datasets to manageable chunks), both Target and Walmart can store their data on a shared server but on separate database processes, they would not have any shared resources like memory or disk, just the virtual environment.  If Walmart and Target went on a shared database server but separate databases, would allow for better resource management between each organization.  If Walmart and Target decided to use the same database (which is unlikely) but hold separate schema, data must have strong access management systems in place.  This may be elected between CVS and Walgreen’s Pharmacy databases, where patient data can be vital, but not impossible to switch from one schema to another, however, this interaction is highly unlikely.   The final structure, highly unlikely for most corporations is sharing databases and schemas.  This final architectural structure is best used for hospitals sharing patient data (AbuKhousa et al, 2012), but, HIPPA must be observed still under this final architecture mode.  Though this is the desired state for some hospitals, it may take years to get to a full system.  Security is a big issue here and will take a ton of developmental hours, but in the long run, it is the cheapest solution available (Connolly & Begg, 2014).

Requirements on services that must be met

Looking at Amazon’s cloud database offering, it should be easy to set up, easy to operate, and easy to scale.  The system should enhance availability and reliability to its live databases compared to in house solutions. There should be software in the databases to back up the database, for recovery at any time. Security patches and maintenance of the underlying hardware and software of the cloud DBMS should be reduced significantly since that is not the burden that should be placed onto the organization. The goal of the Cloud DBMS should be to remove development costs away from managing the DBMS to focus on applications and the data to be stored in the system.  They should also provide infrastructure and hardware as a service to reduce overhead costs in managing these systems.  Amazon’s Relational Database Service can use MySQL, Oracle, SQL Server, or PostgreSQL databases.  Amazon’s DynamoDB is a NoSQL database service.  Amazon’s Redshift costs less than $1K to store a TB of data per year (Varia & Mathew, 2013).

Issues to be resolved during implementation

Rimal et al in 2010, stated some interesting things to consider during a before implementing a Cloud DBMS.  What are the Service-Level Agreements (SLAs) of the supplier Cloud DBMS?  The Cloud DBMS may be up and running 24×7, but if they experience a power outage, what is their SLAs to the organization, as not to impact the organization at all?  What is their backup/replication scheme?  What is their discovery (assists in reusability) schema? What is their load balancing (trying to avoid bottlenecks), especially since most suppliers cater to more than one organization? What does their resource management plan look like? Most cloud DBMS have several copies of the data spread across several servers, so how sure is the vender to ensure no data loss?  What types of security are provided? What is their encryption and decryption strength for the data held within its servers?  How private will the organization’s data be, if hosted on the same server or same database but separate schema?   What are their authorization and Authentication safeguards?  Looking at Varia & Mathew (2013) explain all the cloud DBMS services that Amazon provides, these questions are definitely things that should be addressed for each of their solutions.  Thus, when analyzing a supplier for a cloud DBMS, having technical requirements that meet the Business Goals & Objects (BG&Os) is great to help guide the organization pick the right supplier and solution, given issues that need to be resolved.  Other issues identified came from Chaudhuri (2012): data privacy through access control, auditing, and statistical privacy; allow for data exploration to enable deeper analytics; data enrichment with web 2.0 and social media; query optimization; scalable data platforms; manageability; and performance isolation for multiple organizations occupying the same server.

Data migration strategy & Security Enforcement

When migrating data between organizations into a Cloud DBMS, the taxonomy of the data must be preserved.  Along with taxonomy, one must consider that no data is lost in the transfer, that data is still available to the end-user, before, during and after the migration, and that the transfer is done in a cost-efficient way (Rimal et al 2010).  Furthermore, data migration should be done seamlessly and efficiently as if one were to move between suppliers of services, such that a supplier doesn’t get too entangled into the organization that it is the only solution the organization can see itself as using.  Finally, what type of data do you want to migrate over, mission-critical data may be too precious on certain types of Cloud DBMS, but may be great for a virtualized disaster recovery system?  What type of data to migrate over depends on the needs of a cloud system, to begin with, and what services does the organization want to pay-as-they-go now and in the near future. The type of data to migrate may also depend on the security features provided by the supplier.

Organizational information is a vital resource to any organization, and access to it and maintaining it proprietary is key. If not enforced data like employee social security numbers can be compromised, or credit card numbers of past consumers.  Rimal et al (2009) compared the security considerations in the current cloud systems at the time:

  • Google: uses 128 Bit or higher server authentication
  • GigaSpaces: SSH tunneling
  • Microsoft Azure: Token services
  • OpenNebula: Firewall and virtual private network tunnel

In 2010, Rimal et al, further expanded the security considerations by the suggestion that organizations should look into: authentication and authorization protocols (access management), privacy and federated data, encryption and decryption schemes, etc.


  • AbuKhousa, E., Mohamed, N., & Al-Jaroodi, J. (2012). e-Health cloud: opportunities and challenges. Future Internet, 4(3), 621-645.
  • Chaudhuri, S. (2012, May). What next?: a half-dozen data management research goals for big data and the cloud. In Proceedings of the 31st symposium on Principles of Database Systems (pp. 1-4). ACM.
  • Connolly, T., & Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management, 6th Edition. [VitalSource Bookshelf version]. Retrieved from
  • Rimal, B. P., Choi, E., & Lumb, I. (2009, August). A taxonomy and survey of cloud computing systems. In INC, IMS and IDC, 2009. NCM’09. Fifth International Joint Conference on (pp. 44-51). Ieee.
  • Rimal, B. P., Choi, E., & Lumb, I. (2010). A taxonomy, survey, and issues of cloud computing ecosystems. In Cloud Computing (pp. 21-46). Springer London.
  • Varia, J., & Mathew, S. (2013). Overview of amazon web services. Jan-2014.

P-Hacking: The Menace In Science

In the American Statistician Association (2016a) statement, stated the following conversation:

Q: Why do so many colleges and grad schools teach p = 0.05?

A: Because that’s still what the scientific community and journal editors use.

Q: Why do so many peope still use p = 0.05?

A: Because that’s what they were taught incollege or grad school.

Someone doesn’t need to be studying philosophy, or for the Law School Acceptance Test (LSAT) to see the flaw in that argument.  It’s circular reasoning, and that is the point.  The p-value is being overused when there are so many other ways to measure the strength of the data and it’s significance. Plus, a p = 0.05 is arbitrary and dependent on many fields.  I have seen papers use p = 0.10; p = 0.05, p = 0.01 and rarely p = 0.001.  But, are the results reliable, replicable, and reproducible? There are even studies that manipulate their data to get these elusive p-values…

Scientific research is at the bedrock of pushing society forward. However, not every study’s results published can represent the best of science. Some in the field have tried to alter how long the study lasts, not take into account of a confounding variable that could be causing the results, make the sample size too small to be reliable and allowing luck to be in play, or attempt p-hacking (Adam Ruins Everything, 2017; CrashCourse, 2018; Oliver, 2016).

P-hacking is defined as gathering as many variables as possible, then massaging the huge amounts of data to get a statistically significant result (CrashCourse, 2018; Oliver, 2016). However, that result could be completely meaningless. Similar to when the 538 blog did a p-hacking study called “You can’t trust what you read about nutrition” surveyed 54 people and collected over 1000 variables, found a statistically significant correlation between eating raw tomatoes to Judaism. 538 did this study just to point out the issue of p-hacking (Aschwanden, 2016).

As mentioned earlier, the best way to protect ourselves from p-hacking is to replicate the study and see if we can get similar results to the original study (Adam Ruins Everything, 2017; John Olver, 2016). Unfortunately, in science, there is no prize for fact-checking (John Oliver, 2016). That is why when we do research, we must make sure our results are robust, by testing multiple times if possible.  If it is not possible to do it in your own research, then a replication study is called for by others.  However, Replication studies are rarely ever funded and rarely get published (Adam Ruins Everything, 2017). A great way to do this, is collaborating with scientific peers from multiple universities, work on the same problem, with the same methodology, but different datasets and publish one or a series of papers that confirms a result as replicable and robust.  If we don’t do this, it forces the scientific field to only fund exploratory studies to get developed and published, and the results never get evaluated. Unfortunately, the adage for most scientists is to “publish or perish,” and as Prof. Brian Nosek from Center for Open Science said, “There is NO COST to getting things WRONG. THE COST is not getting them PUBLISHED.” (John Oliver, 2016).

The American Statistical Association (2016b), suggested the following to be used with p-values to give a more accurate representation of the significances:

  • Methods that emphasize estimation over testing
    • Confidence intervals
    • Credibility intervals
    • Prediction intervals
  • Bayesian methods
  • Alternatives measure of evidence
    • Likelihood ratios
    • Bayesian Factors
  • Decision-Theoretic modeling
  • False discovery rates

Have hope, most reputable scientists don’t take the result of one study to heart, but look at in the context of all the work done in that field (Adam Ruins Everything, 2017). Also, most reputable scientists tend to downplay the implications and generalizations of their results when they publish their findings (American Statistical Association, 2016b; Adam Ruins Everything, 2017; CrashCourse, 2018; Oliver, 2016). Looking for those kinds of studies and knowing how p-hacking is done is the best ammunition to defend against spurious results.


Compelling Topics in Advance Topics

  • The evolution of data to wisdom is defined by the DIKW pyramid, where Data is just facts without any context, but when facts are used to understand relationships it generates Information (Almeyer-Stubbe & Coleman, 2014). That information can be used to understand patterns, it can then help build Knowledge, and when that knowledge is used to understand principles, it builds Wisdom (Almeyer-Stubbe & Coleman, 2014; Bellinger, Castro, Mills, n.d.). Building an understanding to jump from one level of the DIKW pyramid, is an appreciation of learning “why” (Bellinger et al., n.d.).
  • The internet has evolved into a socio-technical system. This evolution has come about in five distinct stages:
    • Web 1.0: Created by Tim Berners-Lee in the 1980s, where it was originally defined as a way of connecting static read-only information hosted across multiple computational components primarily for companies (Patel, 2013).
    • Web 2.0: Changed the state of the internet from a read-only state to a read/write state and had grown communities that hold a common interest (Patel, 2013). This version of the web led to more social interaction, giving people and content importance on the web, due to the introduction of social media tools through the introduction of web applications (Li, 2010; Patel, 2013; Sakr, 2014). Web applications can include event-driven and object-oriented programming that are designed to handle concurrent activities for multiple users and had a graphical user interface (Connolly & Begg, 2014; Sandén, 2011).
  • Web 3.0: This is the state the web at 2017. Involves the semantic web that is driven by data integration through the uses of metadata (Patel, 2013). This version of the web supports a worldwide database with static HTML documents, dynamically rendered data, next standard HTML (HTML5), and links between documents with hopes of creating an interconnected and interrelated openly accessible world data such that tagged micro-content can be easily discoverable through search engines (Connolly & Begg, 2014; Patel, 2013). This new version of HTML, HTML5 can handle multimedia and graphical content, which are great for semantic content (Connolly & Begg, 2014). Also, end-users are beginning to build dynamic web applications for others to interact with (Patel, 2013).
    • Web 4.0: It is considered the symbiotic web, where data interactions occur between humans and smart devices, the internet of things (Atzori, 2010; Patel, 2013). These smart devices can be wired to the internet or connected via wireless sensors through enhanced communication protocols (Atzori, 2010). Thus, these smart devices would have read and write concurrently with humans, where the largest potential of web 4.0 has these smart devices analyze data online and begin to migrate the online world into the real world (Patel, 2013).
    • Web 5.0: Previous iterations of the web do not perceive people’s emotion, but one day it could be able to understand a person’s emotional (Patel, 2013). Kelly (2007) predicted that in 5,000 days the internet would become one machine and all other devices would be a window into this machine. In 2007, Kelly stated that this one machine “the internet” has the processing capability of one human brain, but in 5,000 days it will have the processing capability of all the humanity.
  • MapReduce is a framework that uses parallel sequential algorithms that capitalize on cloud architecture, which became popular under the open source Hadoop project, as its main executable analytic engine (Lublinsky et al., 2014; Sadalage & Fowler, 2012; Sakr, 2014). Another feature of MapReduce is that a reduced output can become another’s map function (Sadalage & Fowler, 2012).
  • A sequential algorithm is a computer program that runs on a sequence of commands, and a parallel algorithm runs a set of sequential commands over separate computational cores (Brookshear & Brylow, 2014; Sakr, 2014).
  • A parallel sequential algorithm runs a full sequential program over multiple but separate cores (Sakr, 2014).
    • Shared memory distributed programming: Is where serialized programs run on multiple threads, where all the threads have access to the underlying data that is stored in shared memory (Sakr, 2014). Each thread should be synchronized as to ensure that read and write functions aren’t being done on the same segment of the shared data at the same time. Sandén (2011) and Sakr, (2014) stated that this could be achieved via semaphores (signals other threads that data is being written/posted and other threads should wait to use the data until a condition is met), locks (data can be locked or unlocked from reading and writing), and barriers (threads cannot run on this next step until everything preceding it is completed).
    • Message passing distributed programming: Is where data is stored in one location, and a master thread helps spread chunks of the data onto sub-tasks and threads to process the overall data in parallel (Sakr, 2014). There are explicitly direct send and receive messages that have synchronized communications (Lublinsky et al., 2013; Sakr, 2014).         At the end of the runs, data is the merged together by the master thread (Sakr, 2014).
  • A scalable multi-level stochastic model-based performance analysis has been proposed by Ghosh, Longo, Naik and Trivedi (2012) for Infrastructure as a Service (IaaS) cloud computing. Stochastic analysis and models are a form of predicting how probable an outcome will occur using a form of chaotic deterministic models that help in dealing with analyzing one or more outcomes that are cloaked in uncertainty (Anadale, 2016; Investopedia, n.d.).
  • Three-pool cloud architecture: In IaaS cloud scenario, a request for resources initiates a request for one or more virtual machines to be deployed to access a physical machine (Ghosh et al., 2012). The architecture assumes that physical machines are grouped in pools hot, warm, and cool (Sakr, 2014). The hot pool consists of physical machines that are constantly on, and virtual machines are deployed upon request (Ghosh et al., 2012; Sakr, 2014).   Whereas a warm pool has the physical machines in power saving mode, and cold pools have physical machines that are turned off. For both warm and cold pools setting up a virtual machine is delayed compared to a hot pool, since the physical machines need to be powered up or awaken before a virtual machine is deployed (Ghosh et al., 2012). The optimal number of physical machines in each pool is predetermined by the information technology architects.
  • Data-at-rest is probably considered easier to analyze; however, this type of data can also be problematic. If the data-at-rest is large in size and even if the data does not change or evolve, its large size requires iterative processes to analyze the data.
  • Data-in-motion and streaming data has to be iteratively processed until there is a certain termination condition is reached and it can be reached between iterations (Sakr, 2014). However, Sakr (2014) stated that MapReduce does not support iterative data processing and analysis directly.
    • To deal with datasets that require iterative processes to analyze the data, computer coders need to create and arrange multiple MapReduce functions in a loop (Sakr, 2014). This workaround would increase the processing time of the serialized program because data would have to be reloaded and reprocessed, because there is no read or write of intermediate data, which was there for preserving the input data (Lusblinksy et al., 2014; Sakr, 2014).
  • Data usually gets update on a regular basis. Connolly and Begg (2014) defined that data can be updated incrementally, only small sections of the data, or can be updated completely. This data update can provide its own unique challenges when it comes to data processing.
    • For processing incremental changes on big data, one must split the main computation to its sub-computation, logging in data updates in a memoization server, while checking the inputs of the input data to each sub-computation (Bhatotia et al., 2011; Sakr, 2014). These sub-computations are usually mappers and reducers (Sakr, 2014). Incremental mappers check against the memoization servers, and if the data has already been processed and unchanged it will not reprocess the data, and a similar process for incremental reducers that check for changed mapper outputs (Bhatotia et al., 2011).
  • Brewer (2000) and Gilbert and Lynch (2012) concluded that for a distributed shared-data system you could only have at most two of the three properties: consistency, availability, partition-tolerance (CAP theory). Gilbert and Lynch (2012) describes these three as akin to the safety of the data, live data, and reliability of the data.
    • In a NoSQL distributed database systems (DDBS), it means that partition-tolerance should exist, and therefore administrators should then select between consistency and availability (Gilbert & Lynch, 2012; Sakr, 2014). However, if the administrators focus on availability they can try to achieve weak consistency, or if the administrators focus on consistency, they are planning on having a strong consistency system. Strong consistency ensures that all copies of the data are updated in real-time, whereas weak consistency means that eventually all the copies of the data will be updated (Connolly and Begg, 2014; Sakr, 2014). An availability focus is having access to the data even during downtimes (Sakr, 2014).
  • Volume visualization is used to understand large amounts of data, in other words, big data, where it can be processed on a server or in the cloud and rendered onto a hand-held device to allow for the end user to interact with the data (Johnson, 2011). Tanahashi, Chen, Marchesin and Ma (2010), define a framework for creating an entirely web-based visualization interface (or web application), which leverages the cloud computing environment. The benefit of using this type of interface is that there is no need to download or install the software, but that the software can be accessed through any mobile device with a connection to the internet.


  • Brookshear, G., Brylow, D. (2014). Computer Science: An Overview, (12th ed.). Vitalbook file.
  • Connolly, T., & Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management, (6th ed.). Pearson Learning Solutions. VitalBook file.
  • Ghosh, R., Longo, F., Naik, V. K., & Trivedi, K. S. (2013). Modeling and performance analysis of large scale IaaS clouds. Future Generation Computer Systems, 29 (5), 1216-1234.
  • Gilbert, S., and Lynch N. A. (2012). Perspectives on the CAP Theorem. Computer 45(2), 30–36. doi: 10.1109/MC.2011.389
  • Investopedia (n.d.). Stochastic modeling. Retrieved from
  • Johnson, C. (2011) Visualizing large data sets. TEDx Salt Lake City. Retrieved from
  • Kelly, K. (2007). The next 5,000 days of the web. TED Talk. Retrieved from
  • Li, C. (2010). Open Leadership: How Social Technology Can Transform the Way You Lead, (1st ed.). VitalBook file.
  • Lublinsky, B., Smith, K. T., & Yakubovich, A. (2013). Professional Hadoop Solutions. Vitalbook file.
  • Patel, K. (2013). Incremental journey for World Wide Web: Introduced with Web 1.0 to recent Web 5.0 – A survey paper. International Journal of Advanced Research in Computer Science and Software Engineering, 3(10), 410–417.
  • Sadalage, P. J., Fowler, M. (2012). NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, (1st ed.). Vitalbook file.
  • Sakr, S. (2014). Large Scale and Big Data, (1st ed.). Vitalbook file.
  • Sandén, B. I. (2011). Design of Multithreaded Software: The Entity-Life Modeling Approach. Wiley-Blackwell. VitalBook file.
  • Tanahashi, Y., Chen, C., Marchesin, S., & Ma, K. (2010). An interface design for future cloud-based visualization services. Proceedings of 2010 IEEE Second International Conference on Cloud Computing Technology and Service, 609–613. doi: 10.1109/CloudCom.2010.46


Adv Topics: Possible future study

Application of Artificial Intelligence for real-time cybersecurity threat identification and resolution for network vulnerabilities in the cloud

Motivation: Artificial Intelligence (AI) is an embedded technology, based off of the current infrastructure (i.e. supercomputers), big data, and machine learning algorithms (Cyranoski, 2015; Power, 2015). AI can make use of data hidden in “dark wells” and silos, where the end-user had no idea that the data even existed, to begin with (Power, 2015). The goal of AI is to use huge amounts of data to draw out a set of rules through machine learning that will effectively supplement cyber security experts in identifying and remediating cyberattacks (Cringely, 2013; Power, 2015).

Problem statement: Must consider an attacker’s choices are unknown, if they will be successful in their targets and goals and the physical paths for an attack in the explicit and abstract form, which are hard to do without the use of big data analysis coupled with AI for remediation.

Hypothesis statement:

  • Null: The use of Bayesian Networks and AI cannot be used for both identification and remediation of cyber-attacks that deal with the network infrastructure on a cloud environment.
  • Alternative: The use of Bayesian Networks and AI can be used for both identification and remediation of cyber-attacks that deal with the network infrastructure on a cloud environment.

Proposed solution:

  • New contribution made to the body of knowledge by your proposed solution: The merging of these two technologies can be a first line of defense that can work 24×7 and learn new remediation and identification techniques as time moves forward.

2 research questions:

  • Can the merger of Bayesian Networks and AI be used for both identification and remediation of cyber-attacks that deal with the network infrastructure on a cloud environment? –> This is just based off of the hypothesis.
  • Can the use of Bayesian Networks and AI can be used for both identification and remediation of cyber-attacks that deal with multiple network attacks from various white hat hackers at the same time? –> This is taken from real life. A fortune 500 company is constantly bombarded with thousand if not millions of attempted cyber attackers at a given day. If there is a vulnerability found, it might result in multiple people entering in through that vulnerability and doing serious damage. Could this proposed system handle multiple attacks coming right at the cloud network infrastructure? Essentially providing practitioners some tangible results.


Adv Topics: Big data addressing Security Issues

Cybersecurity attacks are limited by their physical path, the network connectivity and reachability limits, and the attack structure, which is by exploiting a vulnerability that enables an attack (Xie et al., 2010). Previously, automated systems and tools were implemented to deal with moderately skilled cyber-attackers, plus white hat hackers are used to identify security vulnerabilities, but it is not enough to keep up with today’s threats (Peterson, 2012). Preventative measure only deals with newly discoverable items, not the ones that have yet to be discoverable (Fink, Sharifi, Carbonell, 2011). These two methods are preventative measures, with the goal of protecting the big data and cyberinfrastructure used to store and process big data from malicious intent. Setting up these preventative measures are no longer good enough to protect big data and its infrastructure. Thus there has been a migration towards using real-time analysis on monitored data (Glick, 2013). Real-time analysis is concerned with “What is really happening?” (Xie et al., 2010).

If algorithms used to process big data can be pointed towards cyber security, Security Information and Event Management (SIEM), it can add another solution towards identifying cyber security threat (Peterson, 2012). All that big data cyber security analysis will do is make security teams faster to react if they have the right context to the analysis, but it won’t make the security teams act in a more proactive way (Glick, 2013). SIEM has gone above and beyond current cyber security prevention measures, usually by collecting the log data in real time that is generated and processing the log data in real time using algorithms like correlation, pattern recognition, behavioral analysis, and anomaly analysis (Glick, 2013; Peterson, 2012). Glick (2013), reported that data from a variety of sources help build a cyber security risk and threat profile in real-time that can be taken to cyber security teams to react to each threat in real time, but it works on small data sets.

SIEM couldn’t handle the vast amounts of big data and therefore analyzing the next cyber threats came from using tools like Splunk to identify anomalies amongst the data (Glick, 2013). SIEM was proposed for use in the Olympics games, but Splunk was being used for investment banking purposes (Glick, 2013; Peterson, 2012). FireEye is another big data analytics security tool that was used for identifying network threats (Glick, 2013).

  • Xie et al. (2010), proposed the use of Bayesian networks for cyber security analysis. This solution considers that modeling cyber security profiles are difficult to construct and uncertain, plus they built the tool for near real-time systems. That is because Bayesian models try to model cause-and-effect relationships. Using deterministic security models are unrealistic and do not capture the full breadth of a cyber attack and cannot capture all the scenarios for real-time analysis. If the Bayesian models are built to reflect reality, then it could be used for near real-time analysis. In real-time cyber security analysis, analysts must consider an attacker’s choices are unknown or if they will be successful in their targets and goals. Building a modular graphical attack model can help calculate uncertainties, which can be done by decomposing the problem into finite small parts, where realistic data can be used to pre-populate all the parameters. These modular graphical attack models should consider the physical paths in the explicit and abstract form. Thus, the near real-time Bayesian network considers the three important uncertainties introduced in a real-time attack (italicized). Using this method is robust as determined by a holistic sensitivity analysis.
  • Fink et al. (2011), proposed a mashup of crowdsourcing, machine learning, and natural language processing to dealing both vulnerabilities and careless end user actions, for automated threat detection. In their study, they focused on scam websites and cross-site request forgeries. For scam website identification, the concept of using crowdsourced end users to flag certain websites as a scam is key to this process. The goal is that when a new end user approaches the scam website, a popup appears stating “This website is a scam! Do not provide personal information.” The authors’ solution ties data from heterogeneously common web scam blacklist databases. This solution has high precisions (98%), and high recall (98.1%) on their test of 837 manually labeled sites that was cross-validated using a ten-fold cross -validation analysis between the blacklisted database. The current system’s limitation does not address new threats and different sets of threats.

These studies and articles illustrate that the benefit of using big data analytics for cybersecurity analysis provides the following benefits (Fink et al., 2011; Glick, 2013; IBM Software, 2013; Peterson, 2012; Xie et al., 2010):

(a) moving away from preventative cybersecurity and moving towards real-time analysis to become reactive faster to a current threat;

(b) creating security models that more accurately reflect the reality and uncertainty that exists between the physical paths, successful attacks, and unpredictability of humans for near real-time analysis;

(c) provide a robust identification technique; and

(d) reduction of identifying false positives, which eat up the security team’s time.

Thus, helping security teams to solve difficult issues in real-time. However, this is a new and evolving field that is applying big data analytics. Thus it is expected that many tools will be developed, and the most successful tool would be able to provide real-time cybersecurity data analysis with the huge set of algorithms each aimed at studying different types of attacks. It is even possible for one day to see artificial intelligence to become the next new phase of providing real-time cyber security analysis and resolutions.


Adv Topics: Security Issues with Cloud Technology

Big data requires huge amounts of resources to analyze it for data driven decisions, thus there has been a gravitation towards cloud computing to work in this era of big data (Sakr, 2014). Cloud technology is different than personal systems that place different demands on cyber security, where personal systems could have single authority systems and cloud computing systems, have no individual owners, have multiple users, groups rights, and shared responsibility (Brookshear & Brylow, 2014; Prakash & Darbari, 2012). Cloud security can be just as good or better than personal systems because cloud providers could have the economies of scales that can support a budget to have an information security team that many organizations may not be able to afford (Connolly & Begg, 2014). Cloud security can be designed to be independently modular, which is great for heterogenous distributed systems (Prakash & Darbari, 2012).

For cloud computing eavesdropping, masquerading, message tampering, replaying the message, and denial of services are security issues that should be addressed (Prakash & Darbari, 2012). Sakr (2014) stated that exploitation of co-tenancy, a secure architecture for the cloud, accountability for outsourced data, confidentiality of data and computation, privacy, verifying outsourced computation, verifying capability, cloud forensics, misuse detection, and resource accounting and economic attacks are big issues for cloud security. This post will discuss the exploitation of co-tendency and confidentiality of data and computation.

Exploitation of Co-Tenancy: An issue with cloud security is within one of its properties, that it is a shared environment (Prakash & Darbari, 2012; Sakr, 2014). Given that it is a shared environment, people with malicious intent could pretend to be someone they are not to gain access, in other words masquerading (Prakash & Darbari, 2012). Once inside, these people with malicious intent tend to gather information about the cloud system and the data contained within it (Sakr, 2014). Another way these services could be used by malicious people is to use the computational resources of the cloud to carry out denial of service attacks on other people.   Prakash and Darbari (2012) stated that two-factor authentications were used on personal devices and for shared distributed systems, there has been proposed a use of a three-factor authentication. The first two factors are the use passwords and smart cards. The last one could be either biometrics or digital certificates. Digital certificates can be used automatically to reduce end-user fatigue on using multiple authentications (Connolly & Begg, 2014). The third level of authentication helps to create a trusted system. Subsequently, a three-factor authentication could primarily mitigate masquerading. Sakr (2014), proposed using a tool that hides the IP addresses the infrastructure components that make up the cloud, to prevent the cloud for being used if the entry is granted to a malicious person.

Confidentiality of data and computation: If data in the cloud is accessed malicious people can gain information, and change the content of that information. Data stored on the distributed systems are sensitive to the owners of the data, like health care data which is heavily regulated for privacy (Sakr, 2014). Prakash and Darbari (2012) suggested the use of public key cryptography, software agents, XML binding technology, public key infrastructure, and role-based access control are used to deal with eavesdropping and message tampering. This essentially hides the data in such a way that it is hard to read without key items that are stored elsewhere in the cloud system. Sakr (2014) suggested homomorphic encryption may be needed, but warns that the use of encryption techniques increases the cost and time of performance. Finally, Lublinsky, Smith, and Yakubovich (2013), stated that encrypting the network to protect data-in-motion is needed.

Overall, a combination of data encryption, hiding IP addresses of computational components, and three-factor authentication may mitigate some of the cloud computing security concerns, like eavesdropping, masquerading, message tampering, and denial of services. However, using these techniques will increase the time it takes to process big data. Thus a cost-benefit analysis must be conducted to compare and contrast these methods while balancing data risk profiles and current risk models.


  • Brookshear, G., & Brylow, D. (2014). Computer Science: An Overview, (12th ed.). Pearson Learning Solutions. VitalBook file.
  • Connolly, T., & Begg, C. (2014). Database Systems: A Practical Approach to Design, Implementation, and Management, 6th Edition. Pearson Learning Solutions. VitalBook file.
  • Lublinsky, B., Smith, K., & Yakubovich, A. (2013). Professional Hadoop Solutions. Wrox. VitalBook file.
  • Prakash, V., & Darbari, M. (2012). A review on security issues in distributed systems. International Journal of Scientific & Engineering Research, 3(9), 300–304.
  • Sakr, S. (2014). Large scale and big data: Processing and management. Boca Raton, FL: CRC Press.

Adv Topics: Security Issues associated with Big Data

The scientific method helps give a framework for the data analytics lifecycle (Dietrich, 2013). Per Khan et al. (2014), the entire data lifecycle consists of the following eight stages:

  • Raw big data
  • Collection, cleaning, and integration of big data
  • Filtering and classification of data usually by some filtering criteria
  • Data analysis which includes tool selection, techniques, technology, and visualization
  • Storing data with consideration of CAP theory
  • Sharing and publishing data, while understanding ethical and legal requirements
  • Security and governance
  • Retrieval, reuse, and discovery to help in making data-driven decisions

Prajapati (2013), stated the entire data lifecycle consists of the following five steps:

  • Identifying the problem
  • Designing data requirements
  • Pre-processing data
  • Data analysis
  • Data visualizing

It should be noted that Prajapati includes steps that first ask what, when, who, where, why, and how with regards to trying to solve a problem. It doesn’t just dive into getting data. Combining both Prajapati (2013) and Kahn et al. (2014) data lifecycles, provides a better data lifecycle. However, there are 2 items to point out from the above lifecycle: (a) the security phase is an abstract phase because security considerations are involved in stages (b) storing data, sharing and publishing data, and retrieving, reusing and discovery phase.

Over time the threat landscape has gotten worse and thus big data security is a major issue. Khan et al. (2014) describe four aspects of data security: (a) privacy, (b) integrity, (c) availability, and (d) confidentiality. Minelli, Chambers, and Dhiraj (2013) stated that when it comes to data security a challenge to it is understanding who owns and has authority over the data and the data’s attributes, whether it is the generator of that data, the organization collecting, processing, and analyzing the data. Carter, Farmer, and Siegel (2014) stated that access to data is important, because if competitors and substitutes to the service or product have access to the same data then what advantage does that provide the company. Richard and King (2014), describe that a binary notion of data privacy does not exist.  Data is never completely private/confidential nor completely divulged, but data lies in-between these two extremes.  Privacy laws should focus on the flow of personal information, where an emphasis should be placed on a type of privacy called confidentiality, where data is agreed to flow to a certain individual or group of individuals (Richard & King, 2014).

Carter et al. (2014) focused on data access where access management leads to data availabilities to certain individuals. Whereas, Minelli et al. (2013) focused on data ownership. However, Richard and King (2014) was able to tie those two concepts into data privacy. Thus, each of these data security aspects is interrelated to each other and data ownership, availability, and privacy impacts all stages of the lifecycle. The root causes of the security issues in big data are using dated techniques that are best practices but don’t lead to zero-day vulnerability action plans, with a focus on prevention, focus on perimeter access, and a focus on signatures (RSA, 2013). Specifically, certain attacks like denial of service attacks are a threat and root cause to data availability issues (Khan, 2014). Also, RSA (2013) stated that from a sample of 257 security officials felt that the major challenges to security were the lack of staffing, large false positive amounts which creates too much noise, lack of security analysis skills, etc. Subsequently, data privacy issues arise from balancing compensation risks, maintaining privacy, and maintaining ownership of the data, similar to a cost-benefit analysis problem (Khan et al., 2014).

One way to solve security concerns when dealing with big data access, privacy, and ownership is to place a single entry point gateway between the data warehouse and the end-users (The Carology, 2013). The single entry point gateway is essentially middleware, which help ensures data privacy and confidentiality by acting on behalf of an individual (Minelli et al., 2013). Therefore, this gateway should aid in threat detection, assist in recognizing too many requests to the data which can cause a denial of service attacks, provides an audit trail and doesn’t require to change the data warehouse (The Carology, 2013). Thus, the use of middleware can address data access, privacy, and ownership issues. RSA (2013) proposed a solution to use data analytics to solve security issues by automating detection and responses, which will be covered in detail in another post.


  • Carter, K. B., Farmer, D., and Siegel, C. (2014). Actionable Intelligence: A Guide to Delivering Business Results with Big Data Fast! John Wiley & Sons P&T. VitalBook file.
  • Khan, N., Yaqoob, I., Hashem, I. A. T., Inayat, Z. Ali, W. K. M., Alam, M., Shiraz, M., & Gani., A. (2014). Big data: Survey, technologies, opportunities, and challenges. The Scientific World Journal, 2014. Retrieved from
  • Minelli, M., Chambers, M., & Dhiraj, A. (2013). Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today’s Businesses. John Wiley & Sons P&T. VitalBook file.

Adv Topics: MapReduce and Batching Computations

With an increase in data produced by end-user and internet of things connectivity it creates a need for a computational infrastructure that deals with live-streaming big data processing (Sakr, 2014). Real-time data processing requires continuous data input and data output that needs to be processed in a small amount of time (Narsude, 2015). Therefore, once the data comes in it must be processed to be considered a real-time analysis of the data. Real-time data processing deals with a data stream that is defined by an unbounded set of data that can be transformed into stream spouts, which is a bound subset of the stream data (Sakr, 2014).

MapReduce and Hadoop frameworks are amazing with big data-at-rest because it relies on batch jobs with a beginning and an end (Sakr, 2014). However, Narsude (2015) and Sakr (2014), stated that batch processing is not great for real-time stream analytics because stream data doesn’t stop coming in unless an end-user stops it (Sakr, 2014). Batch processing means that the end-user is periodically processing data that has been previously collected to reach a certain size or time-dependent variable (Narsude, 2015).

Streaming data has the following properties (Sakr, 2014): (a) it is generated from multiple places sensuously; (b) has continuous data being produced and in transit; (c) requires continuous parallel processing jobs; (d) online processing of data that isn’t saved, so data analytics lifecycle is applied in real-time to the streaming data; (e) persistently storing streaming data is not optimal; and (f) queries are continuous. Real-time streaming can usually be conducted through two different paradigms (figure 1): processing data one event at a time also known as tuple at a time and keeping data batches small enough to use micro-batching jobs also known as micro-batching (Narsude, 2015).


Figure 1: Illustrating the difference in paradigms in real time processing where the orange dots represent an equal sized event and the blue boxes are the data processing (adapted from Narsude, 2015).   Differing event sizes do exist, which can complicate this image further.

Notice that from figure 1, these two different paradigms create different sets of latency. Thus, real-time processing produces a near real-time output. Therefore, data scientist need to take this into account when picking a tool to handle their real-time data. Some of the many large-scale stream tools include (Sakr, 2014): Aurora, Borealis, IBM System S and IBM Spade, Deduce, StreamCloud, Stormy, and Twitter Storm. The focus here will be on DEDUCE and Twitter Storm.


The DEDUCE architecture offers a middleware solution that can handle streaming data processing and use MapReduce capabilities for real-time data processing (Kumar, Andrade, Gedik, & Wu, 2010; Sakr, 2014). This middleware is using a concept called micro-batching where data is collected in near real-time and is being processed distributive over a batched MapReduce job (Narsude, 2015). Thus, this middleware is using a simplified MapReduce job design for data-at-rest and data-in-motion, where the data stream can come from a variable of an operator or a punctuated input stream and the data output is written in a distributed database system (Kumar et al., 2010; Sakr, 2014). An operator output can come from an end-user, a mapper output or a reducer output (Sakr, 2014).

For its scalability features the middleware creates the illusion to the end-user that the data is stored in one location though it is stored in multiple distributed systems through a common interface (Kumar et al., 2010; Sakr, 2014). DEDUCE’s unified abstraction and runtime has the following customizable features that corresponds to its performance optimization capability (Sakr, 2014): (a) using MapReduce jobs for data flow; (b) re-using MapReduce jobs offline to calibrate data analytics models; (c) optimizing the mapper and reducer jobs under the current computational infrastructure; and (d) being able to control configurations, like update frequency and resource utilization, for the MapReduce jobs.

Twitter Storm

Twitter Storm is an open-source code that is distrusted at GitHub (David, 2011). The Twitter Storm architecture is a distributed and fault-tolerant system that is horizontally scalable to handle parallel processing, guaranteed message processing of all data at least once, fault tolerance to ensure fidelity of results, and programming language agnostic (David, 2011; Sakr, 2014). This system uses processing data one event at a time paradigm to real-time processing (Narsude, 2015).

This software uses data stream spouts and bolts, and a task can be run on either spouts or bolts. Bolts consume a multiple data stream spouts to process and analyze the data to either produce a single output or a new data stream spout (Sakr, 2014). A master node takes care of what stream data and code goes to which spout and bolt through worker node supervisors, and the worker nodes usually work a task(s) per instructions of the worker node supervisor (David, 2011; Sakr, 2014). Considering that the more complex the query, the more data spouts and bolts are needed in the processing topology (figure 2), which helps to address this solution’s scalability features (Sakr, 2014).


Figure 2: A sample Twitter Storm stream to spout to bolt topology (Adapted from Sakr, 2014).

Finally, the performance optimization capability is based on the above sample topology image data, where stream data can be grouped in five different topological spouts and bolts configuration: stream data topology is customizable by the end user; stream data can be randomly distributed, stream data is replicated across all spouts and bolts, one data stream goes to a single bolt directly, or stream data goes into a specified group based on parameters (Sakr, 2014).


Real-time processing is in reality near real-time processes as it depends on the paradigm that is chosen to run the data processing scheme. DEDUCE uses a micro-batching technique, and Twitter Storm uses a tuple at a time. Thus, depending on the problem and project requirements data scientists can use either paradigm and processing scheme that meets their needs.