Social Network Analysis in Telecommunications (Wiley and SAS Business Series)

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For more information about Wiley products, visit www. To Luit, Titus, and Fien. To my parents and parents-in-law. To Cindy, for her unwavering support. One of the consequences is that ana- lytics often focuses too much on complex technologies and statistics rather than long-term value creation.

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It further builds on the extensive research and industry experience of the author team, making it a must-read for anyone using analytics to create value and gain sustainable strategic leverage. This is even more true as we enter a new era of sustainable value creation in which the pursuit of long-term value has to be driven by sustainably strong organizations. The role of corporate employers is evolving as civic involvement and social contribution grow to be key strategic pillars. Acknowledgments It is a great pleasure to acknowledge the contributions and assistance of various colleagues, friends, and fellow analytics lovers to the writing of this book.

This book is the result of many years of research and teaching in business analytics. We are grateful to the active and lively business analytics commu- nity for providing various user fora, blogs, online lectures, and tutori- als, which proved very helpful. We would also like to acknowledge the direct and indirect contributions of the many colleagues, fellow professors, students, researchers, and friends with whom we collaborated during the past years.

Last but not least, we are grateful to our partners, parents, and families for their love, support, and encouragement. We have tried to make this book as complete, accurate, and enjoy- able as possible. Of course, what really matters is what you, the reader, think of it. Please let us know your views by getting in touch.

The authors welcome all feedback and comments—so do not hesitate to let us know your thoughts! The value-centric per- spective toward analytics proposed in this book will be positioned and contrasted with a traditional statistical perspective. This, however, calls for deep insight into the underlying principles of advanced analytical approaches.

As such, we envision that our book will facilitate organizations stepping up to a next level in the adoption of analytics for decision making by embracing the advanced methods introduced in the subsequent chapters of this book. An interesting feature of the approaches discussed in this book is that they have often been developed at the intersection of academia and business, by academics and practitioners joining forces for tun- ing a multitude of approaches to the particular needs and problem characteristics encountered and shared across diverse business settings.

Most of these approaches emerged only after the millennium, which should not be surprising. Since the millennium, we have wit- nessed a continuous and pace-gaining development and an expanding adoption of information, network, and database technologies. Key technological evolutions include the massive growth and success of the World Wide Web and Internet services, the introduction of smart phones, the standardization of enterprise resource planning systems, and many other applications of information technology.

This dramatic change of scene has prospered the development of analytics for business applications as a rapidly growing and thriving branch of science and industry. To achieve the stated objectives, we have chosen to adopt a pragmatic approach in explaining techniques and concepts. We do not focus on providing extensive mathematical proof or detailed algorithms.

Instead, we pinpoint the crucial insights and underlying reasoning, as well as the advantages and disadvantages, related to the practical use of the discussed approaches in a business setting. For this, we ground our discourse on solid academic research expertise as well as on many years of practical experience in elaborating industrial analytics projects in close collaboration with data science professionals.

Throughout the book, a plethora of illustrative examples and case studies are discussed. Example datasets, code, and implementations. Next, the analytics process model is dis- cussed, detailing the subsequent steps in elaborating an analytics project within an organization.


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Business Analytics Data is the new oil is a popular quote pinpointing the increasing value of data and—to our liking—accurately characterizes data as raw material. Data are to be seen as an input or basic resource needing further processing before actually being of use. So, whether data are information depends on whether the data have utility to the recipient. In summary, data typically need to be analyzed, and insight, understanding, or knowledge should be added for data to become useful. Applying basic operations on a dataset may already provide useful insight and support the end user or recipient in decision making.

Bridging Social Network Analysis and Judgment Aggregation | SpringerLink

These basic operations mainly involve selection and aggregation. Both selec- tion and aggregation may be performed in many ways, leading to a plentitude of indicators or statistics that can be distilled from raw data. The following illustration elaborates a number of sales indicators in a retail setting. Typically, visualizations are also adopted to represent indicators and their evolution in time, in easy-to-interpret ways.

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Visualizations provide support by facilitating. Such a report may include a wide variety of indicators that summarize raw sales data. Raw sales data, in fact, concern transactional data that can be extracted from the online transaction processing OLTP system that is operated by the retailer. Remark that calculating these indicators involves basic selection operations on characteristics or dimensions of transactions stored in the database, as well as basic aggregation operations such as sum, count, and average, among others. Personalized dashboards, for instance, are widely adopted in the industry and are very popular with managers to monitor and keep track of business performance.

More advanced analysis of data may further support users and optimize decision making. This is exactly where analytics comes into play. Analytics is a catch-all term covering a wide variety of what are essentially data-processing techniques. Table 1.

Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques

Based on observed variables, the aim is to accurately estimate or predict an unobserved value. The applicable subtype of predictive analytics depends on the type of target variable, which we intend to model as a function of a set of predictor variables. When the target variable is categorical in nature, meaning the variable can only take a limited number of possible values e.

When the task concerns the estimation of a continuous target variable e. Survival analysis and forecasting explicitly account for the time dimension by either predicting the timing of events e. Clustering or segmentation aims at grouping entities e. The basic observations that are being analyzed in this problem setting consist of variable groups of events; for instance, transactions involving various products that are being bought by a customer at a certain moment in time.

The aim of sequence analysis Table 1. T3 crisps, diapers, baby food T4 chocolates, diapers, pizza, apples T5 tomatoes, water, oranges, beer As such, sequence analysis explicitly accounts for the time dimension. These different types of analytics can be applied in quite diverse business and nonbusiness settings and consequently lead to many specialized applications. From an application perspective, this leads to various groups of analytics such as, respectively, fraud analytics, customer or marketing analytics, and credit risk analytics.

A wide range of business applications of analyt- ics across industries and business departments is discussed in detail in Chapter 3. With respect to Table 1. An example of a structured dataset is shown in Table 1. The rows in such a dataset are typically called observations, instances, records, or lines, and represent or collect information on basic entities such as customers, transactions, accounts, or citizens.

The columns are typically referred to as explanatory or predictor variables, characteristics, attributes, predictors, inputs, dimensions, effects, or features.


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The columns contain information on a particular entity as represented by a row in the table. In Table 1. Because of the structure that is present in the dataset in Table 1. Specialized techniques exist that facilitate analysis of unstructured data—for instance, text analytics with applications such as sentiment analysis, video analytics that can be applied for face recognition and incident detection, and network analytics with applications such as community Table 1.

The decision level at which analytics is typically adopted is the operational level, where many customized decisions are to be made that are similar and granular in nature. The decisions involved in developing a business strategy are highly complex in nature and do not match the elementary tasks enlisted in Table 1. A higher-level AI would be required for such purpose, which is not yet at our disposal. At the operational level, however, there are many simple decisions to be made, which exactly match with the tasks listed in Table 1.

The level of optimization depends on the accuracy and validity of the predictions, estimates, or patterns derived from the data. Additionally, as we stress in this book, the quality. Regression Regression models allow us to estimate a continuous target value and in practice are being adopted, for instance, to estimate customer lifetime value.

As is discussed in detail in Chapter 3, analyzing historical customer data allows estimating the future net value of current customers using a regression model. Similar applications involve loss given default modeling as is discussed in Chapter 3, as well as the estimation of software development costs Dejaeger et al.

Survival analysis Survival analysis is being adopted in predictive maintenance applications for estimating when a machine component will fail. Such knowledge allows us to optimize decisions related to machine maintenance—for instance, to optimally plan when to replace a vital component.

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This decision requires striking a balance between the cost of machine failure during operations and the cost of the component, which is preferred to be operated as long as possible before replacing it Widodo and Yang Forecasting A typical application of forecasting involves demand forecasting, which allows us to optimize production planning and supply chain management decisions. For instance, a power supplier needs to be able to balance electricity production and demand by the consumers and for this purpose adopts forecasting or time-series modeling techniques.

These approaches allow an accurate prediction of the short-term evolution of demand based on historical demand patterns Hyndman et al. Clustering facilitates automated decision making by comparing a new transaction to clusters or groups of historical nonfraudulent transactions and by labeling it as suspicious when it differs too much from these groups Baesens et al. Clustering can also be used for identifying groups of similar customers, which facilitates the customization of marketing campaigns.

Association Association analysis is often applied for detecting patterns within analysis transactional data in terms of products that are often purchased together.