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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

In this chapter, we present Synopses Generator, a stream-based processing framework that can provide online summarized representations of trajectories specifically for sailing vessels and flying aircraft Assuming that surveillance data monitoring their locations over a large geographical area is available in a streaming fashion, this novel methodology drops any predictable positions (along trajectory segments of “normal” motion characteristics) with minimal loss in accuracy Effectively, it can keep only those positions conveying salient mobility events (annotated as stop, change in speed, heading, or altitude, etc), identified when the mobility pattern of a given vessel or aircraft changes significantly Moreover, this framework specifies parametrized conditions for detecting such mobility features, as well as suitable heuristics that can eliminate inherent noise and can provide succinct trajectory synopses in one pass over the incoming streaming positions A prototype implementation on top of Apache Flink and Kafka has been set up in modern cluster infrastructures to enable parallelization of the trajectory summarization process against such big mobility data A comprehensive experimental evaluation has been conducted against various surveillance data in the maritime and aviation domain, and offers concrete evidence of its timeliness, scalability, and compression efficiency, with tolerable concessions to the quality of resulting trajectory approximations The resulting compressed trajectories can be particularly useful in efficient online or offline post-processing (eg, mobility analytics, statistics, pattern mining, etc) while also facilitating their comparison irrespectively of differing update frequencies

Trajectory Detection and Summarization over Surveillance Data Streams

In this paper we present an overview of the MetaOn system. The core target of MetaOn is to construct and integrate semantically rich metadata extracted from documents, images and linguistic resources, to facilitate intelligent search and analysis. The MetaOn framework involves ontology-based information extraction and data mining, semiautomatic construction of domain specific ontologies, content-based image indexing and retrieval, and metadata management. The Hellenic history has been chosen as a challenging application case study.

Intelligent Search Assistant: The MetaOn Approach

The survey by Gaede and Guenther [69] annotates a vast list of citations related to multi-dimensional access methods and, in particular, refers to R-trees to a significant extent. In this chapter, we are further focusing on the family of R-trees by enlightening the similarities and differences, advantages and disadvantages of the variations in a more exhaustive manner. As the number of variants that have appeared in the literature is large, we group them according to the special characteristics of the assumed environment or application, and we examine the members of each group.

Dynamic Versions of R-trees

The domain of existentially uncertain spatial data refers to objects that are modelled using an existential probability accompanying spatial data values. An interesting and challenging query type over existentially uncertain data is the search of the nearest neighbor (NN), since the probability of a potential dataset object to be the NN of the query object depends on the locations and probabilities of other points in the same dataset. In this paper, following a statistical approach, we estimate the average number of the NNsrequired to answer probabilistic thresholding NN(PTNN) queries as function of the threshold t, allowing us to utilize existing approaches and propose a cost model for such queries. Based on the same statistical approach, we propose an efficient algorithm for PTNN queries over arbitrarily structured existentially uncertain spatial data. Our experimental study demonstrates the accuracy and efficiency of the proposed techniques.

Cost Models and Efficient Algorithms on Existentially Uncertain Spatial Data

Earthquake data composes an ever increasing collection of earth science information for post-processing analysis. Earth scientists, local or national administration officers and so forth, are working with these data collections for scientific or planning purposes. In this article, we discuss the architecture of a so-called seismic data management and mining system (SDMMS) for quick and easy data collection, processing, and visualization. The SDMMS architecture includes, among others, a seismological database for efficient and effective querying and a seismological data warehouse for OLAP analysis and data mining. We provide template schemes for these two components as well as examples of their functionality towards the support of decision making. We also provide a comparative survey of existing operational or prototype SDMMS.

Yannis Theodoridis

Papers

Trajectory Detection and Summarization over Surveillance Data Streams

Intelligent Search Assistant: The MetaOn Approach

Dynamic Versions of R-trees

Cost Models and Efficient Algorithms on Existentially Uncertain Spatial Data

Seismological Data Warehousing and Mining.