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

Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.

Survey of Wireless Indoor Positioning Techniques and Systems

KEGG(Kyoto Encyclopedia of Genes and Genomes)〔和文〕 (特集 ゲノム医学の現在と未来--基礎と臨床) -- (データベース)

Praise for the Third Edition: "This is one of the best books available. Its excellent organizational structure allows quick reference to specific models and its clear presentation . . . solidifies the understanding of the concepts being presented."IIE Transactions on Operations EngineeringThoroughly revised and expanded to reflect the latest developments in the field, Fundamentals of Queueing Theory, Fourth Edition continues to present the basic statistical principles that are necessary to analyze the probabilistic nature of queues. Rather than presenting a narrow focus on the subject, this update illustrates the wide-reaching, fundamental concepts in queueing theory and its applications to diverse areas such as computer science, engineering, business, and operations research.This update takes a numerical approach to understanding and making probable estimations relating to queues, with a comprehensive outline of simple and more advanced queueing models. Newly featured topics of the Fourth Edition include:Retrial queuesApproximations for queueing networksNumerical inversion of transformsDetermining the appropriate number of servers to balance quality and cost of serviceEach chapter provides a self-contained presentation of key concepts and formulae, allowing readers to work with each section independently, while a summary table at the end of the book outlines the types of queues that have been discussed and their results. In addition, two new appendices have been added, discussing transforms and generating functions as well as the fundamentals of differential and difference equations. New examples are now included along with problems that incorporate QtsPlus software, which is freely available via the book's related Web site.With its accessible style and wealth of real-world examples, Fundamentals of Queueing Theory, Fourth Edition is an ideal book for courses on queueing theory at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners who analyze congestion in the fields of telecommunications, transportation, aviation, and management science.

Fundamentals of Queueing Theory

http://www.eecs.northwestern.edu/~peters/references/LocalizationTechniquesMaoAustralia.pdf

Wireless sensor network localization techniques

Location aware services are becoming attractive with the deployment of next generation wireless networks and broadband multimedia wireless networks especially in indoor and campus areas. To provide location aware services, obtaining the position of a user accurately is important. While it is possible to deploy additional infrastructure for this purpose, using existing communications infrastructure is preferred for cost reasons. Because of technical restrictions, location fingerprinting schemes are the most promising. In this paper we present a systematic study of the performance tradeoff and deployment issues. In this paper we present some experimental results towards such a systematic study and discuss some issues related to the indoor positioning problem.

On indoor position location with wireless LANs

This paper presents TiNA, a scheme for minimizing energy consumption in sensor networks by exploiting end-user tolerance to temporal coherency. TiNA utilizes temporal coherency tolerances to both reduce the amount of information transmitted by individual nodes (communication cost dominates power usage in sensor networks), and to improve quality of data when not all sensor readings can be propagated up the network within a given time constraint. TiNA was evaluated against a traditional in-network aggregation scheme with respect to power savings as well as the quality of data for aggregate queries. Preliminary results show that TiNA can reduce power consumption by up to 50% without any loss in the quality of data.

TiNA: a scheme for temporal coherency-aware in-network aggregation

ACTA is a comprehensive transaction framework that facilitates the formal description of properties of extended transaction models. Specifically, using ACTA, one can specify and reason about (1) the effects of transactions on objects and (2) the interactions between transactions. This article presents ACTA as a tool for the synthesis of extended transaction models, one which supports the development and analysis of new extended transaction models in a systematic manner. Here, this is demonstrated by deriving new transaction definitions (1) by modifying the specifications of existing transaction models, (2) by combining the specifications of existing models, and (3) by starting from first principles. To exemplify the first, new models are synthesized from atomic transactions and join transactions. To illustrate the second, we synthesize a model that combines aspect of the nested- and split-transaction models. We demonstrate the latter by deriving the specification of an open-nested-transaction model from high-level requirements.

Synthesis of extended transaction models using ACTA

Distributed systems are expected to support mobile computations executed over a computer network of fixed and mobile hosts. The authors examine the requirements for structuring such mobile computations that access shared data in a database, argue that open-nesting can better facilitate these requirements, and propose an Open-Nested Transaction model in a mobile environment using the notion of Reporting Transactions and Co-Transactions.

Transaction processing in mobile computing environment

Welcome to MDM 2005 and the beautiful island of Cyprus! It is our great pleasure to welcome you all to the 6th International Conference on Mobile Data Management that is a specialized forum for exchanging ideas on state of the art on mobile data management, systems, and applications. This year the MDM series has reached a new milestone with MDM 2005 taking place for the first time in Europe. Cyprus is a member State of the European Union. The first conferences in the MDM series took place in Hong Kong, MDM 2002 in Singapore, MDM 2003 in Australia, and MDM 2004 in Berkeley, CA of USA.

As the past MDM conferences, MDM 2005 promises to be very exciting and successful, attracting large international participation of researchers and practitioners from databases, networking, and mobile computing communities. The three Program Co-Chairs, Alex Delis (University of Athens, Greece), Ouri Wolfson (University of Illinois at Chicago), and Arkady Zaslavsky (Monash University, Australia), have put together an excellent and selective program. The research program is enhanced by industrial and demo sessions, as well as with four tutorials. It also includes two keynote speeches and two panels focusing on the challenges in the presence of mobility (of users and devices). Our special appreciation to our keynote speakers, Joao Da Silva from the European Commission and Jari Veijalainen from the University of Jyvaskyla, Finalnd. We own our sincere gratitude to all the people who contributed in assembling such a high quality program: the three PC Co-Chairs and the members of their technical program committee, the Industrial Track Chair Apratim Purakayastha (IBM Research, USA) along with industrial track reviewers, the Demo Co-Chairs Avigdor Gal (Technion University, Israel) and Vijay Kumar (University of Missouri at Kansas City), the Tutorials Chair Christian Becker (University of Stuttgart, Germany) and the Panel Chair Evi Pitoura (University of Ioannina, Greece).

Besides coming to Europe for the first time, another new event at MDM this year is the introduction of specialized single-day workshops which are co-located with the MDM conference. Two workshops were selected to complement the MDM 2005: Managing Context Information in Mobile and Pervasive Environments (MCMP05) and Semantics in Mobile Environments (SME05). Consistent with the MDM tradition, the organizers of the two workshops have put together a common, single-track research program consisting of high quality papers.

Panos K. Chrysanthis

Papers

On indoor position location with wireless LANs

TiNA: a scheme for temporal coherency-aware in-network aggregation

Synthesis of extended transaction models using ACTA

Transaction processing in mobile computing environment

Proceedings of the 6th international conference on Mobile data management