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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

COVID-19 is the first pandemic of the modern world causing significant changes to the everyday life of billions of people in all continents. To reduce its expansion, most governments decided to mitigate a great percentage of daily movements of their citizens. For instance, they enforced strict controls (in space, time, etc.) on urban movement whereas they selectively prohibited international air and ground connections. In this short study, we briefly discuss some lessons learned out of this process based on recorded mobility figures, and we raise challenges that are emerging in the post-pandemic era, in the intersection of the sustainable urban mobility and movement data science fields.

Sustainable Urban Mobility in the Post-Pandemic Era (position paper).

Let a tuple of n objects obeying a query graph (QG) be called the n-tuple. The D distance -value of this n-tuple is the value of a linear function of distances of the n objects that make up this n-tuple, according to the edges of the QG. This paper addresses the problem of finding the K n-tuples between n spatial datasets that have the smallest D distance -values, the so-called K-Multi-Way Distance Join Query (K-MWDJQ), where each set is indexed by an R-tree-based structure. This query can be viewed as an extension of K-Closest-Pairs Query (K-CPQ) [4] for n inputs. In addition, a recursive non-incremental branch-and-bound algorithm following a Depth-First search for processing synchronously all inputs without producing any intermediate result is proposed. Enhanced pruning techniques are also applied to the n R-trees nodes in order to reduce the total response time of the query, and a global LRU buffer is used to reduce the number of disk accesses. Finally, an experimental study of the proposed algorithm using real spatial datasets is presented.

Distance Join queries of multiple inputs in spatial databases

The increasing availability of data due to the explosion of mobile devices and positioning technologies has led to the development of efficient management and mining techniques for mobility data. However, the analysis of such data may result in significant risks regarding individuals’ privacy. A typical approach for privacy-aware mobility data sharing aims at publishing an anonymized version of the mobility dataset, operating under the assumption that most of the information in the original dataset can be disclosed without causing any privacy violation. On the other hand, an alternative strategy considers that data stays in-house to the hosting organization and privacy-preserving mobility data management systems are in charge of privacy-aware sharing of the mobility data. In this chapter, we present the state-of-the-art of the latter approach, including systems such as HipStream, Hermes++, and Private-Hermes.

Systems for Privacy-Preserving Mobility Data Management

In recent years, there has been observed an “explosion” of trajectory data production due to the proliferation of GPS-enabled devices, such as mobile phones and tablets This massive-scale data generation has posed new challenges in the data management community in terms of storing, querying, analyzing, and extracting knowledge out of such data Knowledge discovery out of mobility data is essentially the goal of every mobility data analytics task Especially in the maritime and aviation domains, this relates to challenging use-case scenarios, such as discovering valuable behavioral patterns of moving objects, identifying different types of activities in a region of interest, environmental fingerprint, etc In order to be able to support such scenarios, an analyst should be able to apply, at massive scale, several knowledge discovery techniques, such as trajectory clustering, hotspot analysis, and frequent route/network discovery methods

Offline Trajectory Analytics

Vessel Traffic Flow Forecasting (VTFF) is vital for maritime harbor supervision, safety management and collision avoidance. Previous works approach the VTFF problem from two different perspectives: a) directly - by predicting the future traffic based on sequence analysis of historical traffic flow,and b) indirectly - by estimating the future traffic based on future vessel locations produced by vessel route forecasting algorithms. In this work, we introduce the Unified Approach for VTFF (UA-VTFF) method by taking advantage of both the indirect and direct paradigms. Our method, in order to predict the future vessel traffic flow in a time horizon of up to 30 min for a specific space, utilizes the results of the indirect paradigm by feeding them into a model that approaches the VTFF problem directly. UA-VTFF is validated over real Automatic Identification System (AIS) data and compared to baseline methods with quite promising results.

Yannis Theodoridis

Papers

Sustainable Urban Mobility in the Post-Pandemic Era (position paper).

Distance Join queries of multiple inputs in spatial databases

Systems for Privacy-Preserving Mobility Data Management

Offline Trajectory Analytics

Towards a Unified Vessel Traffic Flow Forecasting Framework