Detecting domestic violence.

Over 90% of the case data from police inquiries is stored as unstructured text in police databases. We use the combination of Formal Concept Analysis and Emergent Self Organizing Maps for exploring a dataset of unstructured police reports out of the Amsterdam-Amstelland police region in the Netherlands. In this paper, we specifically aim at making the reader familiar with how we used these two tools for browsing the dataset and how we discovered useful patterns for labelling cases as domestic or as non-domestic violence.Formal concept analysis (FCA); Emergent SOM; Domestic violence; Knowledge discovery in databases; Text mining; Exploratory data analysis;

Academic Analytics in quality assurance using organisational analytical capabilities

The combination of increased environmental complexity and greater quantities of data presents higher education with new problems. Institutions have responded by adopting business analytics approaches from the commercial sector. These approaches, applied in higher education as academic analytics or learning analytics, are designed to improve organisational and educational effectiveness. However, despite extensive research in academic analytics there is an identified need for further work in making analytics “actionable”, a problem of ‘IT in use’. Recent research in business analytics has investigated this problem using a business process orientation combined with an examination of business capabilities for analytics use. Adopting this perspective we apply it to academic analytics in the context of quality assurance, describing an outline approach to the problem of actionable academic analytics

Mining social network data for personalisation and privacy concerns: A case study of Facebook’s Beacon

This is the post-print version of the final published paper that is available from the link below.The popular success of online social networking sites (SNS) such as Facebook is a hugely tempting resource of data mining for businesses engaged in personalised marketing. The use of personal information, willingly shared between online friends' networks intuitively appears to be a natural extension of current advertising strategies such as word-of-mouth and viral marketing. However, the use of SNS data for personalised marketing has provoked outrage amongst SNS users and radically highlighted the issue of privacy concern. This paper inverts the traditional approach to personalisation by conceptualising the limits of data mining in social networks using privacy concern as the guide. A qualitative investigation of 95 blogs containing 568 comments was collected during the failed launch of Beacon, a third party marketing initiative by Facebook. Thematic analysis resulted in the development of taxonomy of privacy concerns which offers a concrete means for online businesses to better understand SNS business landscape - especially with regard to the limits of the use and acceptance of personalised marketing in social networks

Design and Implementation an RFID Customer Shopping Behaviour Mining System

Shopping behavior data is of great an importance in understanding the effectiveness of marketing and merchandising campaigns. Online clothing stores are capable of the capturing customer shopping behavior by analyzing the click streams and customer shopping carts. Retailers within physical clothing stores, however, still lack effective methods to comprehensively identify shopping behaviors. In this study, we show that backscatter signals of passive RFID tags can be exploited to detect and record how customers browse stores, which garments they pay attention to, and which garments they usually pair up. The intuition is that phase readings of tags attached to items will demonstrate distinct yet stable patterns in a time-series when customers look at, pick out, or turn over desired items. We design Shop Miner, a framework that harnesses these unique spatial-temporal correlations of time-series phase readings to detect comprehensive shopping behaviors. We have implemented a prototype of Shop Miner with a COTS RFID reader and four antennas, and tested its effectiveness in two typical indoor environments. Empirical studies from two-week shopping-like data show that Shop Miner is able to identify customer shopping behaviors with high accuracy and low overhead, and is robust to interference

Teaching Tools For Data Analysis

Companies rely on business intelligence and business analytics to support organizational decision making.  Application software packages enable data analysis to help companies pursue a competitive advantage.  Learning to use these tools is not trivial, however, and business schools have added assignments and classes to help their students develop rigorous analytical skills.  This paper describes hands-on, data analysis exercises to support strategic decision making used in an Applied Business Research class that is required for MBA students.  The assignment involves analyzing large volumes of data using the tools of Excel, SQL, and SPSS.  We describe the assignment, data, and exercises that the students perform.  They learn the benefit of analyzing a dataset using different tools and methods, and which tools are most appropriate for what type of analysis. &nbsp

Impact of Analytics in Financial Decision Making: Evidence from a Case Study Approach

Objectives: This study seeks to investigate the impact of analytics in financial decision making in organizations. Prior work: Analytics has emerged as a critical business enabler in today’s competitive market place. Its application has provided businesses with the opportunity to gain a competitive advantage by leveraging the vast amount of data they have available. Analytics is not limited to a particular tool or method however; it encompasses a range of combinations and it is this element that has made analytics such a success factor. Approach: This study uses a case study approach to identify critical areas of business where analytics have played a vital role in financial decision making. Results: Application of analytics in financial decision making is shown to streamline information resulting in making decisions more efficiently and effectively. Implications: This study provides insights in financial decision making using statistical backing which has a vast number of applications in finance functions. As such, areas such as such detecting fraud, budgeting and forecasting, risk management and customer insights need to actively apply analytical tools to better manage and enhance the information gained from these areas. Value: This study integrates the use of information technology tools and packages with financial management with the view of enhancing financial decision flow in organizations

Making sense of business intelligence : proposing a socio-technical framework for improved decision making in not-for-profit organisations

The authors of this paper argue that human intuition alone cannot be relied upon for strategic decision making in today&rsquo;s business environment and that quality data intelligence is an imperative. The proposed project described in this paper is research-in-progress, action design research (ADR), to implement an appropriate information systems (IS) enabling enhanced organisational decision making. ADR is a new research method that draws on action research and design research in an organisational setting. In phase 1 of the project, a sociotechnical &lsquo;sense-making&rsquo; approach is used to gather and analyse information and decision needs in a not-for-profit (NFP) association, Connections ACT. In phase 2, requirements are designed and modelled to build a conceptual framework that guides NFPs in improving business performance and reporting capability. Phase 3 is the evaluative stage when the framework is reflected upon and refined, with intervention in the organisation&rsquo;s processes as a promising outcome.<br /

Big Data and the Data Value Chain: Translating Insights from Business Analytics into Actionable Results - The Case of Unit Load Device (ULD) Management in the Air Cargo Industry

Business intelligence and analytics enjoy a great deal of attention today. However, there is a lack of studies considering the full data value chain from (raw) data through business analytics to valuable decisions, i.e. also scrutinizing the latter stages of the data value chain, namely timely deployment and operational usage of valuable insights as demanded by practice. Following a design science approach, we develop a concept for the fast and flexible integration of valuable insights into daily decision support. A key feature of our concept is to provide valuable insights from business intelligence in an understandable manner to decision makers using a rule-based expert systems approach. In order to demonstrate the feasibility of our concept, we implemented a prototype in a complex real-world scenario, i.e. unit load device (ULD) management in the air cargo industry. This research in progress presents our preliminary findings and outlines the potential of the proposed concept