Using Big Data in Collaborative Learning

Big data emerged as a dominant trend for predictive analytics in many areas of industry and commerce. The study aimed to explore whether similar trends and benefits have been observed in the area of collaborative learning. The study looked at the domains in which the collaborative learning was undertaken. The results of the review found that the majority of the studies were undertaken in the Computing and Engineering or Social Science domains, primarily at undergraduate level. The results indicate that the data collection focus is on interaction data to describe the process of the collaboration itself, rather than on the end product of the collaboration. The student interaction data came from various sources, but with a notable concentration on data obtained from discussion forums and virtual learning environment logs. The review highlighted some challenges; the noisy nature of this data and the need for manual pre-processing of textual data currently renders much of it unsuitable for automated ‘big data' analytical approaches.

Data Stream Mining of Event and Complex Event Streams

Complex Event Processing has been a growing field for the last ten years. It has seen the development of a number of methods and tools to aid in the processing of event streams and clouds though it has also been troubled by the lack of a cohesive definition. This paper aims to layout the technologies surrounding CEP and to distinguish it from the closely related field of Event Stream Processing. It also aims to explore the work done to apply Data Mining Techniques to both of these fields. An outline of stream processing technologies is laid out including the Data Stream Mining techniques that have been adapted for CEP.

Strategic Management of Data and Challenges for Organizations

Public, organizational and personal data has never been so much in the forefront of discussion and attention as at the present time. The term ‘Big Data' (BD) has become part of public discourse, in the press, broadcast media and on the web. Most people in the wider public have very little idea of what it is and what it means but anyone who gives it a thought will see it as contemporary and relevant to life as much as to business. This paper is directed towards the perspectives of people working in, managing and developing organizations which are dedicated to fulfilling their respective purposes. All organizations need to understand their strategic purpose and to develop strategies and tactical responses accordingly. The organizations' purpose and the frameworks and resources adopted are part of its quest for achievement which creates value and worth. BD is a potential and actual source of value.

Semantification of Large Corpora of Technical Documentation

The complexity of machines has grown dramatically in the past years. Today, they are built as a complex functional network of mechanics, electronics, and hydraulics. Thus, the technical documentation became a fundamental source for service technicians in their daily work. The technicians need fast and focused access methods to handle the massive volumes of documentation. For this reason, semantic search emerged as the new system paradigm for the presentation of technical documentation. However, the existent large corpora of legacy documentation are usually not semantically prepared. This fact creates an invincible gap between new technological opportunities and the actual data quality at companies. This chapter presents a novel and comprehensive approach for the semantification of large volumes of legacy technical documents. The approach espescially tackles the veracity and variety existent in technical documentation and makes explicit use of their typical characteristics. The experiences with the implementation and the learned benefits are discussed in industrial case studies.

Data Modeling and Knowledge Discovery in Process Industries

Dispersed data sources, incompatible data formats and a lack of non-ambiguous and machine readable meta-data is a major obstacle in data analytics and data mining projects in process industries. Often, meta-information is only available in unstructured format optimized for human consumption. This contribution outlines a feasible methodology for organizing historical datasets extracted from process plants in a big data platform for the purpose of analytics and machine learning model building in an industrial big data analytics project.

Statistical Features for Extractive Automatic Text Summarization

Automatic Text Summarization (ATS) enables users to save their precious time to retrieve their relevant information need while searching voluminous big data. Text summaries are sensitive to scoring methods, as most of the methods requires to weight features for sentence scoring. In this chapter, various statistical features proposed by researchers for extractive automatic text summarization are explored. Features that perform well are termed as best features using ROUGE evaluation measures and used for creating feature combinations. After that, best performing feature combinations are identified. Performance evaluation of best performing feature combinations on short, medium and large size documents is also conducted using same ROUGE performance measures.

Application of Complex Event Processing Techniques to Big Data Related to Healthcare

Healthcare is a growth area for event processing applications. Computers and information systems have been used for collecting patient data in health care for over fifty years. However, progress towards a unified health care delivery system in the UK has been slow. Big Data, the Internet of Things (IoT) and Complex Event Processing (CEP) have the potential not only to deal with treatment areas of healthcare domain but also to redefine healthcare services. This study is intended to provide a broad overview of where in the health sector, the application of CEP is most used, the data sources that contribute to it and the types of event processing languages and techniques implemented. By systematic review of existing literature on the application of CEP techniques in Healthcare, a number of use cases have been identified to provide a detailed analysis of the most common used case(s), common data sources in use and highlight CEP query language types and techniques that have been considered.

Data Preparation for Big Data Analytics

This chapter provides an overview of methods for preprocessing structured and unstructured data in the scope of Big Data. Specifically, this chapter summarizes according methods in the context of a real-world dataset in a petro-chemical production setting. The chapter describes state-of-the-art methods for data preparation for Big Data Analytics. Furthermore, the chapter discusses experiences and first insights in a specific project setting with respect to a real-world case study. Furthermore, interesting directions for future research are outlined.

How Big Does Big Data Need to Be?

Collecting and storing of as many data as possible is common practice in many companies these days. To reduce costs of collecting and storing data that is not relevant, it is important to define which analytical questions are to be answered and how much data is needed to answer these questions. In this chapter, a process to define an optimal sampling size is proposed. Based on benefit/cost considerations, the authors show how to find the sample size that maximizes the utility of predictive analytics. By applying the proposed process to a case study is shown that only a very small fraction of the available data set is needed to make accurate predictions.

A Framework to Analyze Enterprise Social Network Data

Enterprise Social Networks have a similar set of functionalities as social networking sites but are run as closed applications within a company's intranet. Interacting and communicating on the Enterprise Social Networks, the users, i.e. a company's employees, leave digital traces. The resulting digital record stored in the platform's back end bears great potential for enterprise big data engineering, analytics, and management. This book chapter provides an overview of research in the area of Enterprise Social Networks and categorizes Enterprise Social Network data based on typical functionalities of these platforms. It introduces exemplary metrics as well as a process for the analysis of ESN data. The resulting framework for the analysis of Enterprise Social Network data can serve as a guideline for researchers in the area of Enterprise Social Network analytics and companies interested in analyzing the data stored in the application's back end.