Capture, Create, Manage: The experiences of New Zealand government departments in meeting the requirements of the Electronic Recordkeeping Metadata standard

<p>Research problem: One of the key challenges for electronic recordkeeping is the creation, capture and ongoing management of metadata. The Electronic Recordkeeping Metadata Standard establishes minimum requirements for the New Zealand public sector in accordance with the Public Records Act 2005. This research examines how the recordkeeping systems used by government departments meet the requirements of the metadata standard and what factors influence compliance. Research methodology: This qualitative research surveyed all twenty-nine public sector agencies classified as government departments in the State Sector Act 1988. This was followed up with interviews with seven participants from six departments. Results: This paper found that departments are harnessing the capability of their systems to create, maintain and manage metadata with the resources available. Interviewees showed they look for opportunities to influence the design of new systems and to enhance functionality. Technological factors greatly impact on the extent to which a department can meet the requirements of the standard. A focus on business processes and user needs has resulted in purposeful departures from the standard and a move beyond recordkeeping metadata. Implications: The development of innovative tools and practices by departments has the potential to meet the business/user needs of the organisation and comply with the requirements of the standard. Suggestions have been made as to how the standard could better serve departments dealing with these multiple priorities and technological factors.</p>

Capture, Create, Manage: The experiences of New Zealand government departments in meeting the requirements of the Electronic Recordkeeping Metadata standard

<p>Research problem: One of the key challenges for electronic recordkeeping is the creation, capture and ongoing management of metadata. The Electronic Recordkeeping Metadata Standard establishes minimum requirements for the New Zealand public sector in accordance with the Public Records Act 2005. This research examines how the recordkeeping systems used by government departments meet the requirements of the metadata standard and what factors influence compliance. Research methodology: This qualitative research surveyed all twenty-nine public sector agencies classified as government departments in the State Sector Act 1988. This was followed up with interviews with seven participants from six departments. Results: This paper found that departments are harnessing the capability of their systems to create, maintain and manage metadata with the resources available. Interviewees showed they look for opportunities to influence the design of new systems and to enhance functionality. Technological factors greatly impact on the extent to which a department can meet the requirements of the standard. A focus on business processes and user needs has resulted in purposeful departures from the standard and a move beyond recordkeeping metadata. Implications: The development of innovative tools and practices by departments has the potential to meet the business/user needs of the organisation and comply with the requirements of the standard. Suggestions have been made as to how the standard could better serve departments dealing with these multiple priorities and technological factors.</p>

Cognitive Based Authentication Protocol for Distributed Data and Web Technologies

The objective of the verification process, besides guaranteeing security, is also to be effective and robust. This means that the login should take as little time as possible, and each time allow for a successful authentication of the authorised account. In recent years, however, online users have been experiencing more and more issues with recalling their own passwords on the spot. According to research done in 2017 by LastPass on its employees, the number of personal accounts assigned to one business user currently exceeds 191 profiles and keeps growing. Remembering these many passwords, especially to applications which are not used every week, seems to be impossible without storing them either on paper, in a password manager, or saved in a file somewhere on a PC. In this article a new verification model using a Google Street View image as well as the user’s personal experience and knowledge will be presented. The purpose of this scheme is to assure secure verification by creating longer passwords as well as delivering a ‘password reminder’ already embedded into the login scheme.

Applying Python’s Time Series Forecasting Method in Microsoft Excel – Integration as a Business Process Supporting Tool for Small Enterprises

The paper describes the current state of research, where integration of Microsoft Excel and Python interpreter, gives the business user the right tool to solve chosen business process analysis problems like: forecasting, classification or clustering. The integration is done by using Visual Basic for Application (VBA), as well as XLWings Python’s library. Both mechanisms serve as an interfaces between MS Excel and Python to allow the data exchange between each other. Creating the suitable Graphical User Interface (GUI) in Microsoft Excel, gives the business user opportunity to select specific data analysis method available in Python’s environment and set its parameters, without Python’s programming. Running the method by Python’s interpreter can bring the results, which are hard or even impossible to obtain by using Microsoft Excel only. However, the data analysis methods stored in the Python’s script, which are available to the business user, as well as VBA source code, must be designed and implemented by the data scientist. Sample, basic integration between Microsoft Excel and Python’s interpreter is presented in the paper. To present value-added of the proposed software solution, simple case study according to time series forecasting problem is described, where forecasting errors of different methods available in the Microsoft Excel and Python are presented and discussed. The paper ends with conclusions according to the results of the current researches and suggested directions of further research.

Visual Analytics in Process Mining for Supporting Business Process Improvement

The increasing amounts of data have affected conceptual modeling as a research field. In this context, process mining involves a set of techniques aimed at extracting a process schema from an event log generated during process execution. While automatic algorithms for process mining and analysis are needed to filter out irrelevant data and to produce preliminary results, visual inspection, domain knowledge, human judgment and creativity are needed for proper interpretation of the results. Moreover, a process discovery on an event log usually results in complicated process models not easily comprehensible by the business user. To this end, visual analytics has the potential to enhance process mining towards the direction of explainability, interpretability and trustworthiness in order to better support human decisions. In this paper we propose an approach for identifying bottlenecks in business processes by analyzing event logs and visualizing the results. In this way, we exploit visual analytics in the process mining context in order to provide explainable and interpretable analytics results for business processes without exposing to the user complex process models that are not easily comprehensible. The proposed approach was applied to a manufacturing business process and the results show that visual analytics in the context of process mining is capable of identifying bottlenecks and other performance-related issues and exposing them to the business user in an intuitive and non-intrusive way.

User Growth and Revenue Measurement Fundamentals for a Global Consumer Internet Business

users, and thereby generating revenue. Industries that have been traditionally “offline”, i.e. transportation have also been deeply disrupted and transformed by the evolution of consumer-facing internet services, be it incumbents or be it beginners/new entrants. The following paper outlines the most fundamental growth concepts governing the development of a successful consumer internet business. The objective of the paper is to touch upon two most important pillars of building a successful consumer internet business - User Acquisition and Retention (monetization strategy) and Accounting (measurement strategy). Platformization - which is crucial for a company to scale, is outside the scope of this paper. Each of these pillars are crucial as the business evolves through different growth stages. Risk exposure concepts such as Diversification are outside the scope of this paper.Any consumer-facing business today typically uses the internet as part of its core strategy of acquiring and retaining

Storage systems for IT infrastructure

A review of modern data storage architectures was conducted, the advantages and disadvantages of each of them were given. The data storage systems of the IBM FlashSystem family were analyzed, as well as Spectrum Virtualize software, which is responsible for virtualization, compression, distribution and replication of data stored on the storage system. A mathematical model of the data storage system of IBM Storwize v5030E was developed. Well-known metrics are used to evaluate its performance when using spindle and solid-state drives. The effect of hardware and software data compression on system performance has been experimentally revealed. Recommendations are formulated by which it is possible to determine which media and which technology stack should be used by a business user to complete the tasks assigned to him.