The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

The paradigmatic shift from a Web of manual interactions to a Web of programmatic interactions driven by Web services is creating unprecedented opportunities for the formation of online business-to-business (B2B) collaborations. In particular, the creation of value-added services by composition of existing ones is gaining a significant momentum. Since many available Web services provide overlapping or identical functionality, albeit with different quality of service (QoS), a choice needs to be made to determine which services are to participate in a given composite service. This paper presents a middleware platform which addresses the issue of selecting Web services for the purpose of their composition in a way that maximizes user satisfaction expressed as utility functions over QoS attributes, while satisfying the constraints set by the user and by the structure of the composite service. Two selection approaches are described and compared: one based on local (task-level) selection of services and the other based on global allocation of tasks to services using integer programming.

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QoS-aware middleware for Web services composition

The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery from data. The book Advances in Knowledge Discovery and Data Mining, edited by Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy [FPSSe96], is a collection of later research results on knowledge discovery and data mining. There have been many data mining books published in recent years, including Predictive Data Mining by Weiss and Indurkhya [WI98], Data Mining Solutions: Methods and Tools for Solving Real-World Problems by Westphal and Blaxton [WB98], Mastering Data Mining: The Art and Science of Customer Relationship Management by Berry and Linofi [BL99], Building Data Mining Applications for CRM by Berson, Smith, and Thearling [BST99], Data Mining: Practical Machine Learning Tools and Techniques by Witten and Frank [WF05], Principles of Data Mining (Adaptive Computation and Machine Learning) by Hand, Mannila, and Smyth [HMS01], The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman [HTF01], Data Mining: Introductory and Advanced Topics by Dunham, and Data Mining: Multimedia, Soft Computing, and Bioinformatics by Mitra and Acharya [MA03]. There are also books containing collections of papers on particular aspects of knowledge discovery, such as Machine Learning and Data Mining: Methods and Applications edited by Michalski, Brakto, and Kubat [MBK98], and Relational Data Mining edited by Dzeroski and Lavrac [De01], as well as many tutorial notes on data mining in major database, data mining and machine learning conferences.

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Data Mining: Concepts and Techniques (2nd edition)

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

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Context Aware Computing for The Internet of Things: A Survey

Cyber-security systems, which protect networks and computers against cyber attacks, are becoming common due to increasing threats and government regulation. At the same time, the enormous amount of data gathered by cyber-security systems poses a serious threat to the privacy of the people protected by those systems. To ground this threat, we survey common and novel cyber-security technologies and analyze them according to the potential for privacy invasion. We suggest a taxonomy for privacy risks assessment of information security technologies, based on the level of data exposure, the level of identification of individual users, the data sensitivity and the user control over the monitoring, and collection and analysis of the data. We discuss our results in light of the recent technological trends and suggest several new directions for making these mechanisms more privacy-aware.

The Privacy Implications of Cyber Security Systems: A Technological Survey

Privacy protection in pervasive systems

Discovering sequential relationships in a time sequence is often important to many application domains, including financial, manufacturing, etc. Indeed, a lot of work has been do e in this area (see [1, 6] for an overview). An important aspect for such a discovery process is, however, l argely missing from the literature: namely discovering temporal patterns or relationships that involve multiple t ime granularities. This paper reports the progress in this front. A more detailed study can be found in [4]. In this paper, we focus on algorithms for discovering sequen tial relationships when a rough pattern of relationships is given. The rough pattern (which we term “event s tructure”) specifies what sort of relationships a user is interested in. For example, a user may be interested in “wh ich pairs of events occur frequently one week after another”. The algorithms will find the instances that fit the e v nt structure. They can also be used when more information is filled in the structure, as in in the following example from the stock market domain: “what kind of events frequently follow within 5 trading-days from the o ccurrence of a rise in IBM stocks, when this is preceded, one week before, by the fall of another hi-tech compan y stock”. These algorithms form a core component for a data mining environment. We view the actual data mining process as being done interact ively through a user interface. Data mining requests (in terms of event structures) are issued through t he user interface and processed using the algorithms. Initially, the user will issue a request with a simple struct ure. Complicated structures may be given by the user

Mining Temporal Relationships with Multiple Granularities in Time Sequences

This paper presents a general framework to define time granularity systems. We identify the main dimensions along which different systems can be characterized, and investigate the formal relationships among granularities in these systems. The paper also introduces the notion of a network of temporal constraints with (multiple) granularities emphasizing the semantic and computational differences from constraint networks with a single granularity. Consistency of networks with multiple granularities is shown to be NPdhard in general and approximate solutions for this problem and for the minimal network problem are proposed.

A general framework for time granularity and its application to temporal reasoning

While most activity recognition systems rely on data-driven approaches, the use of knowledge-driven techniques is gaining increasing interest. Research in this field has mainly concentrated on the use of ontologies to specify the semantics of activities, and ontological reasoning to recognize them based on context information. However, at the time of writing, the experimental evaluation of these techniques is limited to computational aspects; their actual effectiveness is still unknown. As a first step to fill this gap, in this paper, we experimentally evaluate the effectiveness of the ontological approach, using an activity dataset collected in a smart-home setting. Preliminary results suggest that existing ontological techniques underperform data-driven ones, mainly because they lack support for reasoning with temporal information. Indeed, we show that, when ontological techniques are extended with even simple forms of temporal reasoning, their effectiveness is comparable to the one of a state-of-the-art technique based on Hidden Markov Models. Then, we indicate possible research directions to further improve the effectiveness of ontology-based activity recognition through temporal reasoning.

http://homes.di.unimi.it/~riboni/comorea11.pdf

Claudio Bettini

Papers

The Privacy Implications of Cyber Security Systems: A Technological Survey

Privacy protection in pervasive systems

Mining Temporal Relationships with Multiple Granularities in Time Sequences

A general framework for time granularity and its application to temporal reasoning

Is ontology-based activity recognition really effective?