Pattern Recognition for Large-Scale Data Processing

One of the main challenges for large-scale computer clouds dealing with massive real-time data is in coping with the rate at which unprocessed data is being accumulated. Transforming big data into valuable information requires a fundamental re-think of the way in which future data management models will need to be developed on the Internet. Unlike the existing relational schemes, pattern-matching approaches can analyze data in similar ways to which our brain links information. Such interactions when implemented in voluminous data clouds can assist in finding overarching relations in complex and highly distributed data sets. In this chapter, a different perspective of data recognition is considered. Rather than looking at conventional approaches, such as statistical computations and deterministic learning schemes, this chapter focuses on distributed processing approach for scalable data recognition and processing.

Generational Differences Relative to Data-Based Wisdom

The purpose of this chapter is to present research findings and address the Generational Differences Relative to Data-Based Wisdom. Data-Based Wisdom is defined as the use of technology, leadership, and culture to create, transfer, and preserve the organizational knowledge embedded in its data, with a view to achieving the organizational vision. So what will comparing Generational Differences effectively do to help achieve organizational vision? If you don't know your history, you are doomed to repeat it; therefore, with the accumulation of ever growing data, understanding the necessary steps to store them properly and ability to retrieve them in an efficient manner are both explicit and tacit knowledge that are outside the scope of the conventional multi-disciplined approach to achieving organizational objectives. With time, technology, leadership, and culture have transformed into more than tangible items, social leadership concepts, and learned behavioral patterns. The latter three ideas have evolved along with the technological advances infused into society as we know it today. Therefore, the value and emphasis to develop and maintain intricate and efficient knowledge management databases suitable to create, transfer, and preserve organizational knowledge embedded in its data has never been more vital. The importance will continue to grow as changes in technology, leadership concepts, and culture continue to inundate.

On the Road to Ephesus

Following a brief review of literature on big data as well as wisdom, this chapter provides a definition of data-based wisdom in the context of healthcare organizations and their visions. The author addresses barriers and ways to overcome barriers to data-based wisdom. Insights from interviews with leading healthcare professionals add practical meaning to the discussion. Finally, future research directions and questions are suggested, including the role of synchronicity and serendipity in data-based wisdom. In this chapter, developing data-based wisdom systems that flourish Wisdom, Virtue, Intellect, and Knowledge are encouraged.

Improving Healthcare with Data-Driven Track-and-Trace Systems

This chapter illustrates the potential of data-driven track-and-trace technology for improving healthcare through efficient management of internal operations and better delivery of services to patients. Track-and-trace can help healthcare organizations meet government regulations, reduce cost, provide value-added services, and monitor and protect patients, equipment, and materials. Two real-world examples of commercially available track-and-trace systems based on RFID and sensors are discussed: a system for counterfeiting prevention and quality assurance in pharmaceutical supply chains and a monitoring system. The system-generated data (such as location, temperature, movement, etc.) about tracked entities (such as medication, patients, or staff) is “big data” (i.e. data with high volume, variety, velocity, and veracity). The chapter discusses the challenges related to data capture, storage, retrieval, and ultimately analysis in support of organizational objectives (such as lowering costs, increasing security, improving patient outcomes, etc.).

Strategic Data-Based Optimization of Working Capital Management in Medium-Sized Firms

The chapter explores whether and to what extent a systemic approach to optimal management of working capital stemming from database wisdom exists in medium enterprises in Serbia as a transition economy. The chapter portrays the level of optimization of all key components of working capital management and addresses indispensable strategic directions for Serbian entrepreneurs and managers that could have broader application in transition context. Ultimately, the chapter explores how to achieve better and more effective results in the development of medium enterprises by optimizing database wisdom for working capital management. As a research paper, the chapter reviews the experiences from Serbia and compares them with achievements in other transition and more advanced economies. In a case study undertaken in Serbia with semi-structured interviews in medium-sized enterprises, the author examines the specific preconditions for increasing the current perceived inefficient use of working capital and extend for discussion an optimization model based on tested hypotheses.

From Data to Vision

This chapter examines the use of big data in the public sector. The public sector pertains to government-related activities. The specific context in this chapter looks at the use of big data at the country level, also described as the federal level. Conceptually, data is processed through a “knowledge pyramid” where data is used to generate information, information generates knowledge, and knowledge begets wisdom. Using this theoretical backdrop, this chapter presents an extension of this model and proposes that the next stage in the pyramid is vision. Vision describes a future plan for the government agency or business, based on the current survey of the organization's environment. To develop these concepts, the use of big data is examined in three different countries. Both opportunities and challenges are outlined, with recommendations for the future. The concepts examined in this chapter are within the constraints of the public sector, but may also be applied to private sector initiatives pertaining to big data.

Generating Data by Gamifying Education

The main focus of this chapter is to present how data generated by gamifying education affects the grading process and student engagement. One of the challenges facing modern society is the overall lack of engagement. This is particularly the case in education when frequent tasks and assignments are not approached with a high level of commitment. A relatively new technique has been developed and is showing great potential in a wide array of fields including education. Gamification is the process of applying game mechanics and a way of thinking to real-world tasks with a goal to improve engagement. Gamifying education generates new types of data that can be used in the process of assessing a student's performance as well as effectively improving engagement. The key to implementing gamification is to understand what types of data can be generated. As the amount of data varies depending on which specific components are applied, the task of its interpretation and contextualisation is vital for a successful outcome.

Aligning and Valuing Data-Based Wisdom Projects

Data-based wisdom projects in the form of Knowledge Management System (KMS) initiatives exhibit a high degree of failure. These failures are caused by technical, people, and organizational challenges. Although the literature provides some degree of guidance for improving the success of KMS initiatives, an overall approach to ensure the KMS meets the needs of the organization does not exist. In this chapter, the common challenges to KMS implementations are discussed and the factors leading to more successful KMS implementation evaluated. Using the findings from the challenges and success factors, a conceptual framework is presented as an approach to increase the success of KMS initiatives. The proposed framework uses strategic alignment and organizational value as drivers in the KMS efforts. The framework applies a multi-dimensional strategy and the Project Portfolio Management (PPM) approach to govern KMS initiatives in an effort to maximize the value offered by the KMS and to ensure the KMS supports the organization's strategies.

A Specification Framework for Big Data Initiatives

Given the emergence of big data technology and its rising popularity, it is important to ensure that the use of this avant-garde technology directly addresses the enterprise goals which are required to maximize the Return-On-Investment (ROI). This chapter aims to address a specification framework for the process of transforming enterprise data into wisdom or actionable information through the use of big data technology. The framework is based on proven methodologies, which consist of three components: Specify, Design, and Refine. The recommended framework provides a systematic, top-down process to extrapolate big data requirements from high-level technical and enterprise goals. The framework also provides a process for managing the quality and relationship between raw data sources and big data products.

Transferring Data to Wisdom in Project Management

More and more companies organize their business through projects, and project management becomes a necessity rather than a luxury and permeates all aspects of business. Therefore, managing knowledge in project environments becomes a key component in project success. In this chapter, basic elements of projects, project management, and knowledge management are introduced, while the main focus of the chapter is put on knowledge transfer between projects and the parent organization. Software tools for project management are also introduced, and some of the most prominent project management software tools are presented. Project Management Office (PMO) as a key player in inter-project knowledge transfer is described, and certain points for future research are given.