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)

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.

Big Data: A Survey

We present a privacy-preserving system for estimating the size of inhomogeneous crowds, composed of pedestrians that travel in different directions, without using explicit object segmentation or tracking. First, the crowd is segmented into components of homogeneous motion, using the mixture of dynamic textures motion model. Second, a set of simple holistic features is extracted from each segmented region, and the correspondence between features and the number of people per segment is learned with Gaussian process regression. We validate both the crowd segmentation algorithm, and the crowd counting system, on a large pedestrian dataset (2000 frames of video, containing 49,885 total pedestrian instances). Finally, we present results of the system running on a full hour of video.

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Privacy preserving crowd monitoring: Counting people without people models or tracking

Recent technological advancements have led to a deluge of data from distinctive domains (e.g., health care and scientific sensors, user-generated data, Internet and financial companies, and supply chain systems) over the past two decades. The term big data was coined to capture the meaning of this emerging trend. In addition to its sheer volume, big data also exhibits other unique characteristics as compared with traditional data. For instance, big data is commonly unstructured and require more real-time analysis. This development calls for new system architectures for data acquisition, transmission, storage, and large-scale data processing mechanisms. In this paper, we present a literature survey and system tutorial for big data analytics platforms, aiming to provide an overall picture for nonexpert readers and instill a do-it-yourself spirit for advanced audiences to customize their own big-data solutions. First, we present the definition of big data and discuss big data challenges. Next, we present a systematic framework to decompose big data systems into four sequential modules, namely data generation, data acquisition, data storage, and data analytics. These four modules form a big data value chain. Following that, we present a detailed survey of numerous approaches and mechanisms from research and industry communities. In addition, we present the prevalent Hadoop framework for addressing big data challenges. Finally, we outline several evaluation benchmarks and potential research directions for big data systems.

Toward Scalable Systems for Big Data Analytics: A Technology Tutorial

We provide here an overview of the new and rapidly emerging research area of privacy preserving data mining. We also propose a classification hierarchy that sets the basis for analyzing the work which has been performed in this context. A detailed review of the work accomplished in this area is also given, along with the coordinates of each work to the classification hierarchy. A brief evaluation is performed, and some initial conclusions are made.

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State-of-the-art in privacy preserving data mining

Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting at the same time sensitive information. Several data mining techniques, incorporating privacy protection mechanisms, have been developed that allow one to hide sensitive itemsets or patterns, before the data mining process is executed. Privacy preserving classification methods, instead, prevent a miner from building a classifier which is able to predict sensitive data. Additionally, privacy preserving clustering techniques have been recently proposed, which distort sensitive numerical attributes, while preserving general features for clustering analysis. A crucial issue is to determine which ones among these privacy-preserving techniques better protect sensitive information. However, this is not the only criteria with respect to which these algorithms can be evaluated. It is also important to assess the quality of the data resulting from the modifications applied by each algorithm, as well as the performance of the algorithms. There is thus the need of identifying a comprehensive set of criteria with respect to which to assess the existing PPDM algorithms and determine which algorithm meets specific requirements.

In this paper, we present a first evaluation framework for estimating and comparing different kinds of PPDM algorithms. Then, we apply our criteria to a specific set of algorithms and discuss the evaluation results we obtain. Finally, some considerations about future work and promising directions in the context of privacy preservation in data mining are discussed.

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A Framework for Evaluating Privacy Preserving Data Mining Algorithms

The widespread use of personal software systems and the boom of the so-called Web 2.0 is erasing the distinctions between those who create software products and those who use or consume them. End users are increasingly involved in the design and development of the tools they use. Unfortunately, there is a high incidence of errors in applications developed by end users. In this paper, a view on end-user development is outlined, which identifies the communication gap between end users and professional software developers as one main source of errors. The spectrum of users that lie between pure end users and professional developers is examined. In particular, the focus is on a particular type of end users that are very active in shaping software tools to their needs without being aware that they are programming: in short, they are unwitting programmers. Their characteristics and their need of appropriate development techniques and environments are analyzed. Finally, the meta-design participatory approach we have developed is briefly described to show how it fills the communication gap and well supports the activities of unwitting programmers.

End users as unwitting software developers

Nowadays very different people use computer systems for their daily working activities, but also for fun and entertainment or only to satisfy their information needs. Designers are doing their best to create computer systems that work as end users expect, but it must be honestly admitted that they often fail and end users have all rights to complain. In order to improve this situation and create better systems, participatory approaches have been proposed, which involve end users in the design and development process. However, this solution is not without flaws, mainly because timing and ways of users' participation are very critical. In this paper we discuss our approach to create working systems , which is based on a star model of the software life cycle that drives system design, development and evolution, since software design and development is seen as an evolutive process, driven by end-users activities in the real life. System development does not end with its first release; it is experimented by its end users and further evolved on the basis of their feedbacks. End users are truly engaged in the software life cycle as co-designers and experimenters of the software tools they will use in various application domains.

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Supporting End Users to Be Co-designers of Their Tools

Rich Internet applications have removed most of the constraints of Web 1.0 while giving users more responsiveness and advanced browsing and interaction experiences. These new horizons, however, raise many challenges for people with disabilities or using limited hardware and software technologies, whose risk to be excluded from the benefits deriving from advanced web applications. To address this problem, WCAG 2.0 guidelines have been released as the newest World Wide Web Consortium recommendation for accessible web content, and WAI-ARIA is a candidate recommendation which provides reference specifications for accessible rich Internet applications. However, both specifications contain a huge amount of information that often discourages most web designers from dealing with accessibility issues. Moreover, guidelines are suitable and usually adopted to judge a design solution a posteriori, but they do not suggest how to face a design problem constructively. This paper proposes a design pattern language for accessibility. The language can be regarded as a universal design resource for helping web designers create accessible rich Internet applications compliant with the most recent standards. Knowledge representation through design patterns reflects the problem-solving approach usually followed by software and web designers, while pattern organization in a structured language aims to guide web designers throughout the design process. The language has been implemented as an accessible rich Internet application itself, thus allowing designers with disabilities to participate in web design. In order to evaluate the design pattern language, a three-step process was carried out including: (1) a heuristic analysis with a group of human---computer interaction experts, (2) a survey study with a group of web designers, and (3) a validation on the field with two designers who have been requested to apply the language in real design cases.

Loredana Parasiliti Provenza

Papers

State-of-the-art in privacy preserving data mining

A Framework for Evaluating Privacy Preserving Data Mining Algorithms

End users as unwitting software developers

Supporting End Users to Be Co-designers of Their Tools

A universal design resource for rich Internet applications based on design patterns