Evaluation frameworks for information systems integration: from a semiotic lens

Information systems integration aims at the interaction, information exchange and interoperability between information systems, devices and units. Research efforts have contributed in evaluation of information systems integration on the development of evaluation frameworks. To improve the usability and measurability of evaluation, a review of existing evaluation frameworks including their evolution and classifications of different interoperability levels is conducted. The theory of organisational semiotics is used for a comparative analysis of the frameworks and future work.

Institutional pressures on financial services firms: the role of information systems in regulatory compliance

The financial crisis of 2008 led to new international regulatory controls for the governance, risk and compliance of financial services firms. Information systems play a critical role here as political, functional and social pressures may lead to the deinstitutionalization of existing structures, processes and practices. This research examines how an investment management system is introduced by a leading IT vendor across eight client sites in the post-crisis era. Using institutional theory, it examines changes in working practices occurring at the environmental and organizational levels and the ways in which technological interventions are used to apply disciplinary effects in order to prevent inappropriate behaviors. The results extend the constructs of deinstitutionalization and identify empirical predictors for the deinstitutionalization of compliance and trading practices within financial organizations.

Design of a unified data with business rules storage model for OLTP and OLAP systems

This paper reviews the literature concerning the practice of using Online Analytical Processing (OLAP) systems to recall information stored by Online Transactional Processing (OLTP) systems. Such a review provides a basis for discussion on the need for the information that are recalled through OLAP systems to maintain the contexts of transactions with the data captured by the respective OLTP system. The paper observes an industry trend involving the use of OLTP systems to process information into data, which are then stored in databases without the business rules that were used to process information and data stored in OLTP databases without associated business rules. This includes the necessitation of a practice, whereby, sets of business rules are used to extract, cleanse, transform and load data from disparate OLTP systems into OLAP databases to support the requirements for complex reporting and analytics. These sets of business rules are usually not the same as business rules used to capture data in particular OLTP systems. The paper argues that, differences between the business rules used to interpret these same data sets, risk gaps in semantics between information captured by OLTP systems and information recalled through OLAP systems. Literature concerning the modeling of business transaction information as facts with context as part of the modelling of information systems were reviewed to identify design trends that are contributing to the design quality of OLTP and OLAP systems. The paper then argues that; the quality of OLTP and OLAP systems design has a critical dependency on the capture of facts with associated context, encoding facts with contexts into data with business rules, storage and sourcing of data with business rules, decoding data with business rules into the facts with the context and recall of facts with associated contexts. The paper proposes UBIRQ, a design model to aid the co-design of data with business rules storage for OLTP and OLAP purposes. The proposed design model provides the opportunity for the implementation and use of multi-purpose databases, and business rules stores for OLTP and OLAP systems. Such implementations would enable the use of OLTP systems to record and store data with executions of business rules, which will allow for the use of OLTP and OLAP systems to query data with business rules used to capture the data. Thereby ensuring information recalled via OLAP systems preserves the contexts of transactions as per the data captured by the respective OLTP system.

Benchmarking or business opportunity: Using milk recording data to expose your clients' weaknesses

Routine milk recording data, often covering many years, are available for approximately half the dairy herds of England and Wales. In addition to milk yield and quality, these data include production events that can be used to derive objective Key Performance Indicators (KPI) describing a herd's fertility and production. Recent developments in information systems give veterinarians and other technical advisers access to these KPIs on-line. In addition to reviewing individual herd performance, advisers can establish local benchmark groups to demonstrate the relative performance of similar herds in the vicinity. The use of existing milk recording data places no additional demands on farmer's time or resources. These developments could also readily be exploited by universities to introduce veterinary undergraduates to the realities of commercial dairy production.

Evolving features-algorithms knowledge map to support NIDS data intelligence and learning loop architecting – a generalised approach to NIDS pattern feature space knowledge processing

This paper describes a proposed new approach to the Computer Network Security Intrusion Detection Systems (NIDS) application domain knowledge processing focused on a topic map technology-enabled representation of features of the threat pattern space as well as the knowledge of situated efficacy of alternative candidate algorithms for pattern recognition within the NIDS domain. Thus an integrative knowledge representation framework for virtualisation, data intelligence and learning loop architecting in the NIDS domain is described together with specific aspects of its deployment.

Towards the design of a unified semantics model for OLTP and OLAP systems

The concepts of on-line transactional processing (OLTP) and on-line analytical processing (OLAP) are often confused with the technologies or models that are used to design transactional and analytics based information systems. This in some way has contributed to existence of gaps between the semantics in information captured during transactional processing and information stored for analytical use. In this paper, we propose the use of a unified semantics design model, as a solution to help bridge the semantic gaps between data captured by OLTP systems and the information provided by OLAP systems. The central focus of this design approach is on enabling business intelligence using not just data, but data with context.

A Conceptual Model for Group Support Systems in Local Councils

This paper proposes a conceptual model of a context-aware group support system (GSS) to assist local council employees to perform collaborative tasks in conjunction with inter- and intra-organisational stakeholders. Most discussions about e-government focus on the use of ICT to improve the relationship between government and citizen, not on the relationship between government and employees. This paper seeks to expose the unique culture of UK local councils and to show how a GSS could support local government employer and employee needs.

Analysis of the dominant enterprise information assurance mechanisms

The purpose of this study is to analyse current data continuity mechanisms employed by the target group of businesses and to identify any inadequacies in the mechanisms as a whole. The questionnaire responses indicate that 47% of respondents do perceive backup methodologies as important, with a total of 70% of respondents having some backup methodology already in place. Businesses in Moulton Park perceive the loss of data to have a significant effect upon their business’ ability to function. Only 14% of respondents indicated that loss of data on computer systems would not affect their business at all, with 54% of respondents indicating that there would be either a “major effect” (or greater) on their ability to operate. Respondents that have experienced data loss were more likely to have backup methodologies in place (53%) than respondents that had not experienced data loss (18%). Although the number of respondents clearly affected the quality and conclusiveness of the results returned, the level of backup methodologies in place appears to be proportional to the company size. Further investigation is recommended into the subject in order to validate the information gleaned from the small number of respondents.

An overview of interactive visual data mining techniques for knowledge discovery

n the past decade, the analysis of data has faced the challenge of dealing with very large and complex datasets and the real-time generation of data. Technologies to store and access these complex and large datasets are in place. However, robust and scalable analysis technologies are needed to extract meaningful information from these datasets. The research field of Information Visualization and Visual Data Analytics addresses this need. Information visualization and data mining are often used complementary to each other. Their common goal is the extraction of meaningful information from complex and possibly large data. However, though data mining focuses on the usage of silicon hardware, visualization techniques also aim to access the powerful image-processing capabilities of the human brain. This article highlights the research on data visualization and visual analytics techniques. Furthermore, we highlight existing visual analytics techniques, systems, and applications including a perspective on the field from the chemical process industry.

Unified framework for construction of rule based classification systems

Automatic generation of classification rules has been an increasingly popular technique in commercial applications such as Big Data analytics, rule based expert systems and decision making systems. However, a principal problem that arises with most methods for generation of classification rules is the overfit-ting of training data. When Big Data is dealt with, this may result in the generation of a large number of complex rules. This may not only increase computational cost but also lower the accuracy in predicting further unseen instances. This has led to the necessity of developing pruning methods for the simplification of rules. In addition, classification rules are used further to make predictions after the completion of their generation. As efficiency is concerned, it is expected to find the first rule that fires as soon as possible by searching through a rule set. Thus a suit-able structure is required to represent the rule set effectively. In this chapter, the authors introduce a unified framework for construction of rule based classification systems consisting of three operations on Big Data: rule generation, rule simplification and rule representation. The authors also review some existing methods and techniques used for each of the three operations and highlight their limitations. They introduce some novel methods and techniques developed by them recently. These methods and techniques are also discussed in comparison to existing ones with respect to efficient processing of Big Data.

Case study on the maintenance of a construction monitoring using USN-based data acquisition

In recent years, there has been an increasing interest in the adoption of emerging ubiquitous sensor network (USN) technologies for instrumentation within a variety of sustainability systems. USN is emerging as a sensing paradigm that is being newly considered by the sustainability management field as an alternative to traditional tethered monitoring systems. Researchers have been discovering that USN is an exciting technology that should not be viewed simply as a substitute for traditional tethered monitoring systems. In this study, we investigate how a movement monitoring measurement system of a complex building is developed as a research environment for USN and related decision-supportive technologies. To address the apparent danger of building movement, agent-mediated communication concepts have been designed to autonomously manage large volumes of exchanged information. In this study, we additionally detail the design of the proposed system, including its principles, data processing algorithms, system architecture, and user interface specifics. Results of the test and case study demonstrate the effectiveness of the USN-based data acquisition system for real-time monitoring of movement operations.

An investigation of the role of business analysts in IS development

Business analysis has developed since the early 1990s as an IS discipline that is concerned with understanding business problems, defining requirements and evaluating relevant solutions. However, this discipline has had limited recognition within the academic community and little research has been conducted into the practices and standards employed by business analysts. This paper reports on a study into business analysis that considered the activities conducted and the outcomes experienced on IS projects. Senior business analysts were interviewed in order to gain insights into the business analyst role and the techniques and approaches applied when conducting this work. The Context, Content, Process, Outcomes framework was adopted as a basis for developing the interview questions. The data collected was analysed using the template analysis technique and the template was based upon this framework. Additional themes concerning aspects of business analysis that may contribute to IS success emerged during data analysis. These included the key business analysis activities and the skills business analysts require to perform these activities. The organisational attitude was also identified as a key factor in enabling the use and contribution of business analysis.