The following article found on https://www.econtentpro.com/blog/, talks about abuses that can lead to various forms of plagiarism. eContent Pro (2019) is a really great article showcasing that there is more than one way to plagiarise. However, they did not provide examples to showcase each case, nor explained the nuance in case 2 all that well (eContent Pro, 2019):
- Self-plagiarism
- Overreliance on Multiple Sources
- Patchwriting
- Overusing the same source
The following is my attempt to do just that.
Example of Self-plagiarism
If I were to use the following two paragraphs verbatim in a new paper or as a book chapter … even though these are my words from Hernandez (2017a), it is considered self-plagiarism. It is good to recycle your work cited page, it is not good to recycle your words, just like you would recycle plastic bottles.
Chapter 1: An Introduction to Data Analytics
Data analytics has existed before 1854. Snow (1854) had a theory on how cholera outbreaks occur, and he was able to use that theory to remove the pump handle off of a water pump, where that water pump had been contaminated in the summer of 1854. He had set out to prove that his hypothesis on how cholera epidemics originated from was correct, so he then drew his famous spot maps for the Board of Guardians of St. James’ parish in December 1854. These maps were showed in his eventual 2nd edition of his book “On the Mode of Communication of Cholera” (Brody, Rip, Vinten-Johansen, Paneth, & Rachman, 2000; Snow, 1855). As Brody et al. (2000) stated, this case was one of the first famous examples of the theory being proven by data, but the earlier usage of spot maps has existed.
However, the use of just geospatial data analytics can be quite limiting in finding a conclusive result if there is no underlying theory as to why the data is being recorded (Brody et al., 2000). Through the addition of subject matter knowledge and subject matter relationships before data analytics, context can be added to the data for which it can help yield better results (Garcia, Ferraz, & Vivacqua, 2009). In the case of Snow’s analysis, it could have been argued by anyone that the atmosphere in that region of London was causing the outbreak. However, Snow’s original hypothesis was about the transmission of cholera through water distribution systems, the data then helped support his hypothesis (Brody et al., 2000; Snow 1854). Thus, the suboptimal results generated from the outdated Edisonian-esque, which is a test-and-fail methodology, can prove to be very costly regarding Research and Development, compared to the results and insights gained from text mining and manipulation techniques (Chonde & Kumara, 2014).
Example of Overreliance on Multiple Sources
The following was taken from my Dissertation (Hernandez, 2017b). There is definitely an overreliance on sources here. As with any dissertation, master’s thesis, or even interdisciplinary work. However, my voice still shines through. That is where the line is drawn by eContent Pro (2019): Is the author’s voice still present?
In this excerpt, it shows how I gathered multiple methodologies, from multiple sources and combined them all to form a best practice for data preprocessing. Another word for this process is called Synthesizing. Not one source had all the components, and listing which source contained which parts of the best practice methodologies was the purpose of these three paragraphs. If my voice wasn’t present in these paragraphs, then it would be considered plagiarism.
Collecting the raw and unaltered real world data is the first step of any data or text
mining research study (Coralles et al., 2015; Gera & Goel, 2015; He et al., 2013; Hoonlor, 2011; Nassirtoussi et al., 2014). Next, preprocessing raw text data is needed, because raw text data files are unsuitable for predictive data analytics software tools like WEKA (Hoonlor, 2011; Miranda, n.d.). Barak and Modarres (2015), Miranda (n.d.), and Nassirtoussi et al. (2014) concluded that in both data and text mining, data preprocessing has the most significant impact on the research results.Raw data can have formats that change across time, therefore converting the data into one common format for analysis is necessary for data analytics (Mandrai & Barkar, 2014). Also, the removal of HTML tags from web-based data sources allows for the removal of extraneous data points that can provide unpredictable results (Netzer et al., 2012). Finally, deciding on a strategy about how to deal with missing or defective data fields can aid in mitigating noise from the results (Barak & Modarres, 2015; Fayyad et al., 1996; Mandrai & Barskar, 2014; Netzer, 2012). Furthermore, to gain the most insights surrounding a research problem, data from multiple data
sources should be collected and integrated (Corrales et al., 2015).…
Predictive data analytics tools can analyze unstructured text data after the preprocessing step. Preprocessing involves tokenization, stop word removal, and word-normalization (Hoonlor, 2011; Miranda, n.d.; Nassirtoussi et al., 2014; Nassirtoussi et al., 2015; Pletscher-Frankild et al., 2015; Thanh & Meesad, 2014). Tokenization is when a body of text is reduced to a set of units, phrases, or groups of keywords for analysis (Hoonlor, 2011; Miranda, n.d.; Nassirtoussi et al., 2014; Nassirtoussi et al., 2015; Pletscher-Frankild et al., 2015; Thanh & Meesad, 2014). For
example, the term eyewall replacement would be considered one token, rather than two words or two different tokens. Stopword removal is the removal of the words that add no value to the predictive analytics algorithm from the body of text; these words are prepositions, articles, and conjunctions (Hoonlor, 2011; Miranda, n.d.; Nassirtoussi et al., 2014; Nassirtoussi et al., 2015; Thanh & Meesad, 2014). Miranda (n.d.) stated that sometimes stop-word removals could also be context-dependent because some contextual words can yield little to no value in the analysis. For instance, meteorological forecast models in this study are considered context-dependent stopwords. Lastly, word-normalization transforms the letters into a body of text to one single case type and removes the conjugations of words (Hoonlor, 2011; Miranda, n.d.; Nassirtoussi et al., 2014; Nassirtoussi et al., 2015; Thanh & Meesad, 2014). For example, stemming the following words cooler, coolest, and colder becomes cool-, which heightens the fidelity of the results due to the reduction of dimensionalities.
Example of Pathwriting and overusing the same source
This self-created meta-post for this post, which happens to be a curation post for Service Operations KPIs and CSF. The words below have been lifted from various sections of:
- AXELOS (2011). ITIL® Service Operations. Retrieved from https://www.axelos.com/store/book/itil-service-operations
Each sample Critical Success Factors (CSFs) is followed by a small number of typical Key Performance Indicators (KPIs) that support the CSF. These KPIs should not be adopted without careful consideration. Each organization should develop KPIs that are appropriate for its level of maturity, its CSFs and its particular circumstances. Achievement against KPIs should be monitored and used to identify opportunities for improvement, which should be logged in the CSI register for evaluation and possible implementation.
Service Operations: Ensures that services operate within agreed parameters, when it’s interrupted they restore services ASAP
Request Fulfillment Management: Request Fulfillment is responsible for
- Managing the initial contact between users and the Service Desk.
- Managing the lifecycle of service requests from initial request through delivery of the expected results.
- Managing the channels by which users can request and receive services via service requests.
- Managing the process by which approvals and entitlements are defined and managed for identified service requests (future).
- Managing the supply chain for service requests and assisting service providers in ensuring that the end-to-end ddelivery is managed according to plan.
- Working with the Service Catalog and Service Portfolio managers to ensure that all standard service requests are appropriately defined and managed in the service catalog (future).
- CSF Requests must be fulfilled in an efficient and timely manner that is aligned to agreed service level targets for each type of request
o KPI The mean elapsed time for handling each type of service request
o KPI The number and percentage of service requests completed within agreed target times
o KPI Breakdown of service requests at each stage (e.g. logged, work in progress, closed etc.)
o KPI Percentage of service requests closed by the service desk without reference to other levels of support (often referred to as ‘first point of contact’)
o KPI Number and percentage of service requests resolved remotely or through automation, without the need for a visit
o KPI Total numbers of requests (as a control measure)
o KPI The average cost per type of service request
- CSF Only authorized requests should be fulfilled
o KPI Percentage of service requests fulfilled that were appropriately authorized
o KPI Number of incidents related to security threats from request fulfilment activities
- CSF User satisfaction must be maintained
o KPI Level of user satisfaction with the handling of service requests (as measured in some form of satisfaction survey)
o KPI Total number of incidents related to request fulfilment activities
o KPI The size of current backlog of outstanding service requests.
Incident Management: Incident Management is responsible for the resolution of any incident, reported by a tool or user, which is not part of normal operations and causes or may cause a disruption to or decrease in the quality of a service.
- CSF Resolve incidents as quickly as possible minimizing impacts to the business
o KPI Mean elapsed time to achieve incident resolution or circumvention, broken down by impact code
o KPI Breakdown of incidents at each stage (e.g. logged, work in progress, closed etc.)
o KPI Percentage of incidents closed by the service desk without reference to other levels of support (often referred to as ‘first point of contact’)
o KPI Number and percentage of incidents resolved remotely, without the need for a visit
o KPI Number of incidents resolved without impact to the business (e.g. incident was raised by event management and resolved before it could impact the business)
- CSF Maintain quality of IT services
o KPI Total numbers of incidents (as a control measure)
o KPI Size of current incident backlog for each IT service
o KPI Number and percentage of major incidents for each IT service
- CSF Maintain user satisfaction with IT services
o KPI Average user/customer survey score (total and by question category)
o KPI Percentage of satisfaction surveys answered versus total number of satisfaction surveys sent
- CSF Increase visibility and communication of incidents to business and IT support staff
o KPI Average number of service desk calls or other contacts from business users for incidents already reported
o KPI Number of business user complaints or issues about the content and quality of incident communications
- CSF Align incident management activities and priorities with those of the business
o KPI Percentage of incidents handled within agreed response time (incident response-time targets may be specified in SLAs, for example, by impact and urgency codes)
o KPI Average cost per incident
- CSF Ensure that standardized methods and procedures are used for efficient and prompt response, analysis, documentation, ongoing management and reporting of incidents to maintain business confidence in IT capabilities
o KPI Number and percentage of incidents incorrectly assigned
o KPI Number and percentage of incidents incorrectly categorized
o KPI Number and percentage of incidents processed per service desk agent
o KPI Number and percentage of incidents related to changes and releases.
Problem Management: Problem Management is responsible for the activities required to
- Diagnose the root cause of incidents.
- Determine the resolution to related problems.
- Perform trend analysis to identify and resolve problems before they impact the live environment.
- Ensure that resolutions are implemented through the appropriate control procedures, especially change management and release management.
Problem Management maintains information about problems and appropriate workarounds and resolutions to help the organization reduce the number and impact of incidents over time. To do this, Problem Management has a strong interface with Knowledge Management and uses tools such as the Known Error Database.
- CSF Minimize the impact to the business of incidents that cannot be prevented
o KPI The number of known errors added to the KEDB
o KPI The percentage accuracy of the KEDB (from audits of the database)
o KPI Percentage of incidents closed by the service desk without reference to other levels of support (often referred to as ‘first point of contact’)
o KPI Average incident resolution time for those incidents linked to problem records
- CSF Maintain quality of IT services through elimination of recurring incidents
o KPI Total numbers of problems (as a control measure)
o KPI Size of current problem backlog for each IT service
o KPI Number of repeat incidents for each IT service
- CSF Provide overall quality and professionalism of problem handling activities to maintain business confidence in IT capabilities
o KPI The number of major problems (opened and closed and backlog)
o KPI The percentage of major problem reviews successfully performed
o KPI The percentage of major problem reviews completed successfully and on time
o KPI Number and percentage of problems incorrectly assigned
o KPI Number and percentage of problems incorrectly categorized
o KPI The backlog of outstanding problems and the trend (static, reducing or increasing?)
o KPI Number and percentage of problems that exceeded their target resolution times
o KPI Percentage of problems resolved within SLA targets (and the percentage that are not!)
o KPI Average cost per problem.
Event Management: These processes have planning, design, and operations activity. Event Management is responsible for any aspect of Service Management that needs to be monitored or controlled and where the monitoring and controls can be automated. This includes:
- Configuration items.
- Environmental controls.
- Software licensing.
- Security.
- Normal operational activities.
Event Management includes defining and maintaining Event Management solutions and managing events.
- CSF Detecting all changes of state that have significance for the management of CIs and IT services
o KPI Number and ratio of events compared with the number of incidents
o KPI Number and percentage of each type of event per platform or application versus total number of platforms and applications underpinning live IT services (looking to identify IT services that may be at risk for lack of capability to detect their events)
- CSF Ensuring all events are communicated to the appropriate functions that need to be informed or take further control actions
o KPI Number and percentage of events that required human intervention and whether this was performed
o KPI Number of incidents that occurred and percentage of these that were triggered without a corresponding event
- CSF Providing the trigger, or entry point, for the execution of many service operation processes and operations management activities
o KPI Number and percentage of events that required human intervention and whether this was performed
- CSF Provide the means to compare actual operating performance and behaviour against design standards and SLAs
o KPI Number and percentage of incidents that were resolved without impact to the business (indicates the overall effectiveness of the event management process and underpinning solutions)
o KPI Number and percentage of events that resulted in incidents or changes
o KPI Number and percentage of events caused by existing problems or known errors (this may result in a change to the priority of work on that problem or known error)
o KPI Number and percentage of events indicating performance issues (for example, growth in the number of times an application exceeded its transaction thresholds over the past six months)
o KPI Number and percentage of events indicating potential availability issues (e.g. failovers to alternative devices, or excessive workload swapping)
- CSF Providing a basis for service assurance, reporting and service improvement
o KPI Number and percentage of repeated or duplicated events (this will help in the tuning of the correlation engine to eliminate unnecessary event generation and can also be used to assist in the design of better event generation functionality in new services)
o KPI Number of events/alerts generated without actual degradation of service/functionality (false positives – indication of the accuracy of the instrumentation parameters, important for CSI).
Access Management: Access Management aims to grant authorized users the right to use a service, while preventing access to non-authorized users. The Access Management processes essentially execute policies defined in [[IT Security Management |Information Security Management]]. Access Management is sometimes also referred to as ”Rights Management” or ”Identity Management”.
- CSF Ensuring that the confidentiality, integrity and availability of services are protected in accordance with the information security policy
o KPI Percentage of incidents that involved inappropriate security access or attempts at access to services
o KPI Number of audit findings that discovered incorrect access settings for users that have changed roles or left the company
o KPI Number of incidents requiring a reset of access rights
o KPI Number of incidents caused by incorrect access settings
- CSF Provide appropriate access to services on a timely basis that meets business needs
o KPI Percentage of requests for access (service request, RFC etc.) that were provided within established SLAs and OLAs
- CSF Provide timely communications about improper access or abuse of services on a timely basis
o KPI Average duration of access-related incidents (from time of discovery to escalation).
Resources:
- eContent Pro (2019). Plagiarism: Abusing References. Retrieved from https://www.econtentpro.com/blog/plagiarism-abusing-references/
- Hernandez (2017a). Survey of the different type of data analytics algorithms. International Journal of Strategic Information Technology and Applications.
- Hernandez (2017b). Application of predictive analytics on Semistructured North Atlantic Tropical Cyclone forecasts. Dissertation, Colorado Technical University, Denver, CO.
Dr. Skylar (Sky) Hernandez 