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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

Data Mining - Concepts and Techniques.

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

Virtual agents are an important element of virtual environments that enhance their believability and autonomy. Nowadays, there are a number of architectures and languages for designing virtual agents and scripting their control process, but the majority of them cannot cope with the vagueness that characterizes a designer’s description of a location or direction using natural language, and, thus, fail to demonstrate complex spatial behavior in dynamic environments. We propose a framework for handling spatial vagueness in virtual agent control, which uses binary fuzzy relations to represent vague location descriptions and a fuzzy rule-based system for the agent control. We present a prototype implementation and a case study, in which the proposed framework is successfully used for a virtual agent’s locomotion in a dynamic environment.

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Handling spatial vagueness in virtual agent control

The massive-scale data generation of positioning (tracking) messages, collected by various surveillance means, has posed new challenges in the field of mobility data analytics in terms of extracting valuable knowledge out of this data. One of these challenges is online cluster analysis, where the goal is to unveil hidden patterns of collective behaviour from streaming trajectories, such as co-movement and co-stationary (aka anchorage) patterns. Towards this direction, in this paper, we demonstrate MaSEC (Moving and Stationary Evolving Clusters), a system that discovers valuable behavioural patterns as above. In particular, our system provides a unified solution that discovers both moving and stationary evolving clusters on streaming vessel position data in an online mode. The functionality of our system is evaluated over two real-world datasets from the maritime domain.

MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories

A major performance goal of a DBMS is to minimize the number of I/O’s (i.e., blocks or pages transferred) between the disk and main memory. One way to achieve this goal is to minimize the number of I/O’s when answering a query. Note that many queries reference only a small portion of the records in a database file. For example the query: “find the employees who reside in Santa Monica” references only a fraction of the records in the Employee relation. It would be very inefficient to have the database system sequentially read all the pages of the Employee file and check the residence field of each employee record for the name “Santa Monica”. Instead the system should be able to locate the pages with “Santa Monica” employee records directly. To allow such fast access additional data structures called access methods are designed per database file. There are two fundamental access methods, namely indexing and hashing. The most widely used indexing scheme is the B+-tree. Hashing is also common, in particular in its Extendible and Linear Hashing schemes. We also describe two multi-attribute access methods, the k-d tree and the Grid File. Finally, we discuss an approach that is popular for document searching, the Inverted File.

Fundamental Access Methods

From the pioneering works on spatiotemporal databases back in ‘90s to the era of Big Mobility Data Analytics nowadays, in this paper we try to follow the thread of research objectives and initiatives in the field. Initially, we provide a flashback to the 25 past years (though from a personal, hence, biased point of view). Then, we discuss in brief the challenges related to mobility data processing, management, analytics, and visualization to be addressed in modern applications tracking populations of moving objects in real time.

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Yannis Theodoridis

Papers

Handling spatial vagueness in virtual agent control

MaSEC: Discovering Anchorages and Co-movement Patterns on Streaming Vessel Trajectories

Fundamental Access Methods

Learning from Our Movements – The Mobility Data Analytics Era

Handling spatial vagueness in virtual agent control.