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Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

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Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword -- Preface -- Authors -- List of Major GIS Datasets and Program Files -- 1. Why Hospital Service Areas? -- 1.1 Hospital Service Area ( HSA) as a Functional Region -- 1.2 Value of HSAs -- 1.3 Study Area and Data -- 1.4 Overview of Remaining Chapters -- 2. Estimating Distance and Travel Time Matrices in GIS -- 2.1 Measures of Distance and Travel Time -- 2.2 Computing Distance and Drive Time Matrices in ArcGIS Pro -- 2.2.1 Computing Euclidean and Geodesic Distance Matrices in ArcGIS Pro -- 2.2.2 Computing a Drive Time Matrix in ArcGIS Pro -- 2.3 Estimating a Transit Travel Time Matrix in ArcGIS Pro -- 2.4 Estimating Drive Time and Transit Time Matrices by Google Maps API -- 2.5 Estimating a Large Drive Time Matrix by a Differential Sampling Approach -- 2.5.1 Estimating Preliminary Inter-zonal Times -- 2.5.2 Calibrating Inter-zonal Times on Randomly Sampled OD Pairs by Google Maps API -- 2.5.3 Appending Intra-zonal Times -- 2.6 Summary -- 3. Analysis of Spatial Behavior of Health Care Utilization in Distance Decay -- 3.1 Distance Decay Functions -- 3.2 Value of Analyzing Distance Decay Effects in Health Care Studies -- 3.3 Deriving the Distance Decay Functions by the Spatial Interaction Model -- 3.3.1 Estimating the Spatial Interaction Model -- 3.3.2 Distance Decay Effects across Geographic Areas in Florida -- 3.4 Deriving the Distance Decay Functions by a Complementary Cumulative Distribution Curve -- 3.4.1 Estimating the Complementary Cumulative Distribution Function -- 3.4.2 Distance Decay Effects Across Population Groups in Florida -- 3.5 Summary -- 4. Delineating Hospital Service Areas by the Dartmouth Method -- 4.1 History and Applications of Dartmouth HSAs and HRRs -- 4.2 The Dartmouth Method for Defining HSAs and HRRs.

1. Introduction to global information warfare -- 2. From information warfare to information operations and cyber warfare -- 3. War stories from the digital battlefield -- 4. Pre 9/11 -- 5. Effect of 9/11 and US Homeland Security (DHS) -- 6. Nation-state defensive and offensive information warfare capabilities : North America -- 7. Nation-state defensive and offensive information warfare capabilities : Middle East nation-states -- 8. Nation-state defensive and offensive information warfare capabilities : Asia Pacific region -- 9. Nation-state defensive and offensive information warfare capabilities : Europe -- 10. Nation-state defensive and offensive information warfare capabilities : the Russian Federation -- 11. International organizations' defensive and offensive information warfare capabilities -- 12. Nonstate actors -- 13. The history of technology -- 14. Corporate and national resilience -- 15. Awareness -- 16. The Tallinn manual -- 17. A look at the future : the crystal ball.

Data mining is already incorporated into the business processes in many sectors such as health, retail, automotive, finance, telecom and insurance as well as in government. This technology is well established in applications such as targeted marketing, customer churn detection and market basket analysis. It is also emerging as an important technology in a wide range of new application areas, such as social media, social networks and sensor networks. These areas pose new challenges both in terms of the nature of available data and the underlying support technology. This book contains extended v.

In this paper, the authors present a three-level mediator based framework for linked data integration. In the approach, the mediated schema is represented by a domain ontology, which provides a conceptual representation of the application. Each relevant data source is described by a source ontology, published on the Web according to the Linked Data principles. Each source ontology is rewritten as an application ontology, whose vocabulary is restricted to be a subset of the vocabulary of the domain ontology. The main contribution of the paper is an algorithm for reformulating a user query into sub-queries over the data sources. The reformulation algorithm exploits inter-ontology links to return more complete query results. The approach is illustrated by an example of a virtual store mediating access to online booksellers.

AbstractUnderlying information about failure, including observations made in free text, can be a good source for understanding, analyzing, and extracting meaningful information for determining causation. The unstructured nature of natural language expression demands advanced methodology to identify its underlying features. There is no available solution to utilize unstructured data for risk assessment purposes. Due to the scarcity of relevant data, textual data can be a vital learning source for developing a risk assessment methodology. This work addresses the knowledge gap in extracting relevant features from textual data to develop cause–effect scenarios with minimal manual interpretation. This study applies natural language processing and text‐mining techniques to extract features from past accident reports. The extracted features are transformed into parametric form with the help of fuzzy set theory and utilized in Bayesian networks as prior probabilities for risk assessment. An application of the proposed methodology is shown in microbiologically influenced corrosion‐related incident reports available from the Pipeline and Hazardous Material Safety Administration database. In addition, the trained named entity recognition (NER) model is verified on eight incidents, showing a promising preliminary result for identifying all relevant features from textual data and demonstrating the robustness and applicability of the NER method. The proposed methodology can be used in domain‐specific risk assessment to analyze, predict, and prevent future mishaps, ameliorating overall process safety.

What is data-driven HR? -- The evolution of intelligent (and super intelligent) HR -- Data-driven strategy : making a business case for more intelligent HR -- Capitalising on the data explosion : identifying key sources of HR-relevant data -- Data-driven HR tools : turning data into insights with HR analytics -- Potential pitfalls : looking at data privacy, transparency and security -- Data-driven recruitment -- Data-driven employee engagement -- Data-driven employee safety and wellness -- Data-driven learning and development -- Data-driven performance management -- The future of data-driven HR