CiteULike: votis's library [56 articles]

The state of CRM adoption by the financial services in the UK: an empirical investigation

Bill Karakostas — 2006-12-14T09:01:27-00:00

Information & Management, Vol. 42, No. 6. (September 2005), pp. 853-863.

In recent years, organisations have begun to realise the importance of knowing their customers better. Customer relationship management (CRM) is an approach to managing customer related knowledge of increasing strategic significance. The successful adoption of IT-enabled CRM redefines the traditional models of interaction between businesses and their customers, both nationally and globally. It is regarded as a source for competitive advantage because it enables organisations to explore and use knowledge of their customers and to foster profitable and long-lasting one-to-one relationships. This paper discusses the results of an exploratory survey conducted in the UK financial services sector; it discusses CRM practice and expectations, the motives for implementing it, and evaluates post-implementation experiences. It also investigates the CRM tools functionality in the strategic, process, communication, and business-to-customer (B2C) organisational context and reports the extent of their use. The results show that despite the anticipated potential, the benefits from such tools are rather small.

Customer relationship management (CRM) in e-government: a relational perspective

Shan-Ling Pan — 2006-08-27T05:33:48-00:00

Decision Support Systems, Vol. 42, No. 1. (October 2006), pp. 237-250.

The case of the National IT Literacy Program (NITLP) as part of Singapore's e-government initiative serves to illustrate the evolution of strategic customer relationship management (CRM) practices. The role of CRM has remained relatively consistent even though its practices have evolved in response to both environmental and technological changes. This study introduces the concepts of relational incentive, relational value and relational tool that position indirect communications as an important contender to direct communications for organizational relationship building. This study adopts a relational perspective with which to formulate a managerial strategy for CRM that is independent of direct organizational involvement.

CIDOC CRM in Action – Experiences and Challenges

Philipp Nussbaumer — 2007-09-02T23:57:17-00:00

Research and Advanced Technology for Digital Libraries (2007), pp. 532-533.

Integration of metadata from heterogeneous sources is a major issue when connecting cultural institutions to digital library networks. Uniform access to metadata is impeded by the structural and semantic heterogeneities of the metadata and metadata schemes used in the source systems. In this paper we discuss the methodologies we applied to ingest proprietary metadata into the BRICKS digital library network and to process CIDOC CRM metadata in terms of search and retrieval, and how we strove to hide the semantic complexity from the end-user while exploiting the semantic richness of the underlying metadata.

An analysis of consumer power on the Internet

Umit — 2008-07-01T15:19:40-00:00

Technovation, Vol. 27, No. 1-2. ( 2007), pp. 47-56.

The industrial revolution was to manufacturers what the digital revolution is to consumers. What we are seeing today is a renegotiation of the relationships between companies and consumers, and a fundamental recasting of conventional marketing in favor of the consumer. This study, therefore, discusses consumer power in marketing theory and analyzes consumer power sources and changing power dynamics with case studies. Finally, it contributes to theory by investigating power dynamics in each stage of the consumer decision-making process.

Multi-channel strategy in business-to-business markets: Prospects and problems

Bert Rosenbloom — 2008-05-22T23:33:49-00:00

Industrial Marketing Management, Vol. 36, No. 1. (January 2007), pp. 4-9.

Multi-channel marketing strategy has become a major force in business-to-business distribution channels, especially since the option of Internet-based online channels emerged less than a decade ago. Making products and services available to business markets via a wide array of different channels can provide increased levels of customer choice and service. But the task of coordinating and integrating multiple channels that operate at high levels of efficiency has forced managers responsible for channel management to deal with a variety of challenging issues. These include the role of e-commerce in the multi-channel structure, finding an optimal channel mix, creating synergies across channels, building strategic alliances, creating sustainable competitive advantages, managing more complex supply chains, dealing with conflict, and providing the leadership necessary to attain well integrated multiple channels.

AgentStra: an Internet-based multi-agent intelligent system for strategic decision-making

Shuliang Li — 2008-07-01T15:16:20-00:00

Expert Systems with Applications, Vol. 33, No. 3. (October 2007), pp. 565-571.

This article reports the development and experimental evaluation of an Internet-enabled multi-agent prototype system, called AgentStra, for developing marketing strategies, competitive strategies and associated IT/IS/e-commerce strategies. Firstly, the multi-agent architecture of the AgentStra system is presented with relevant strategy agents described. Secondly, the logical flow and screen examples of the system execution are illustrated with guidelines on coupling AgentStra with human judgement proposed. Thirdly, the pilot evaluation of the system's effectiveness and efficiency is documented with preliminary findings discussed. Finally, the conclusions are drawn with further research work envisaged.

A possibilistic-valued multi-criteria decision-making support for marketing activities in e-commerce: Feedback Based Diagnosis System

Afef Denguir-Rekik — 2008-07-01T15:14:23-00:00

European Journal of Operational Research, Vol. In Press, Corrected Proof

In this paper we propose a multi-criteria decision making support system, called a "Feedback Based Diagnosis System" (FBDS), to aid the marketing team of an e-commerce (EC) organisation in its activities. The FBDS database is composed of customers' satisfaction measures. These measures are related to the different services an EC offers to its customers. Thus, they constitute a multi-criteria (MC) evaluation of EC performances. In the general framework of recommender systems, these available MC evaluations are considered as useful information for other customers to help them to objectively, rationally and exhaustively assess and compare the numerous ECs among the ones likely to meet their needs. Our FBDS is not concerned with improving or automating such a recommendation process for customers. Indeed, it is merely EC management team oriented. In fact, the MC feedback database is used to diagnose the EC health and improve its strategy. In the proposed FBDS, a possibilistic framework is combined with the multi criteria representation to capture the variability and the divergence of customers' evaluations w.r.t. each criterion. Then, an aggregation based on a weighted arithmetic mean (WAM) is proposed to obtain a synthetic appraisal of ECs. The WAM aggregation models the strategy agreed on by the EC management team. Computing the synthesis score of an EC consists in propagating the uncertainty related to its partial scores through the WAM. The possibilistic representation guarantees that no information is lost in the collective evaluation process by the consumers' community. However, diagnosis indicators are finally proposed to the marketing team to make the interpretation of some possibilistic results more comprehensive when necessary.

Recommendations for e-commerce systems in the tourism industry of sub-Saharan Africa

Tonderai Maswera — 2008-07-01T15:13:28-00:00

Telematics and Informatics, Vol. In Press, Corrected Proof

The recommendations described in this paper are a continuation of research previously reported in the Telematics and Informatics journal. This paper explains how the tourism organisations from sub-Saharan Africa can evolve their websites into marketing tools and how they can overcome the impediments to e-commerce adoption and usage. The recommendations also explain how the other major players within the economies of these countries can make the environment conducive for e-commerce development and growth so that the tourism organisations from this region can break into the lucrative international tourism market. The recommendations were tested by sending them to the African organisations and experts in e-commerce and tourism who have worked in, or are currently based in Africa, south of the Sahara. The results showed most organisations and experts who responded think that these recommendations will help African tourism organisations adopt and use e-commerce. African tourism organisations that intend to implement or are in the process of implementing e-commerce systems should follow the recommendations outlined in this paper to help sub-Saharan Africa reach its tourism potential.

Global marketing effectiveness via alliances and electronic commerce in business-to-business markets

Saeed Samiee — 2008-01-14T19:43:09-00:00

Industrial Marketing Management, Vol. 37, No. 1. (January 2008), pp. 3-8.

Numerous changes in the global business climate have intensified global competition through new forms of competition as well as the addition of new competitors. As a result, domestic and international firms have to develop and implement marketing strategies that are aligned with the current global competitive realities. This study explores the influence of three overarching developments that stand out as having a dominating role in the shifting international competitive landscape: (1) the rapid growth of global business activities by existing firms and new entrants, for example, through increased international outsourcing (i.e., the intensification of importing activities); (2) the transition to managing supply chain systems through greater coordination of entire distribution channels, alliances, and relational exchanges; and (3) the emergence and increased strategic deployment of electronic forms of exchange, particularly with respect to information access, storage, and retrieval, as means of more efficient management of domestic and global network of operations and market intelligence. Managerial and research implications of these trends are discussed.

International issues of the design and usage of websites for e-commerce: Hotel and airline examples

Terri Lituchy — 2008-07-01T15:07:41-00:00

Journal of Engineering and Technology Management, Vol. 25, No. 1-2. ( 2008), pp. 93-111.

Technology has globalized businesses. International business is difficult due to differences in languages and cultures. In terms of technology, in some countries, it is difficult to get data; while in others, data is input in different ways. This paper examines how international differences affect website design, implementation and usage. We analyze websites of airlines and hotels. While some websites take into consideration language and culture differences, we find significant room for improvement in both industries. We found support for localization strategy in marketing, communication and transactions. Theoretical and practical implications, including language translators, currency converters and pull-down fields, are discussed.

E-commerce adoption of travel and tourism organisations in South Africa, Kenya, Zimbabwe and Uganda

Tonderai Maswera — 2008-07-01T14:53:53-00:00

Telematics and Informatics, Vol. 25, No. 3. (August 2008), pp. 187-200.

Africa, with its great wealth in wildlife and unique resorts, can benefit from the ever increasing user population of the Internet, particularly in the USA and Western Europe where most of the tourists to Africa come from (Internet World Stats, 2004. World Internet Users and Population Stats. <http://www.internetworldstats.com/stats>.). A first survey was carried out to find the nature and extent of e-commerce adoption by tourism organisations from South Africa, Kenya, Zimbabwe and Uganda which are all popular tourist destinations in eastern and southern Africa. For comparison, a second survey of tourism organisations from USA and Western Europe was also carried out. A total of 373 websites from the four African countries and 180 from the USA and Western Europe were accessed and then evaluated against a list of e-commerce features. The surveys revealed that few of the African organisations are embracing e-commerce and that, although some websites were comparable to those of their western counterparts, the majority had room for considerable improvements. The African websites were found to be generally informative but lacked interactive facilities for online transactions. It is recommended that these African organisations evolve their websites into marketing tools to capitalise on the potential Internet market.

Evaluating collaborative filtering recommender systems

Jonathan Herlocker — 2005-04-27T17:40:41-00:00

ACM Trans. Inf. Syst., Vol. 22, No. 1. (January 2004), pp. 5-53.

Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.

Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions

G Adomavicius — 2005-04-26T12:49:12-00:00

Knowledge and Data Engineering, IEEE Transactions on, Vol. 17, No. 6. (2005), pp. 734-749.

This paper presents an overview of the field of recommender systems and describes the current generation of recommendation methods that are usually classified into the following three main categories: content-based, collaborative, and hybrid recommendation approaches. This paper also describes various limitations of current recommendation methods and discusses possible extensions that can improve recommendation capabilities and make recommender systems applicable to an even broader range of applications. These extensions include, among others, an improvement of understanding of users and items, incorporation of the contextual information into the recommendation process, support for multcriteria ratings, and a provision of more flexible and less intrusive types of recommendations.

E-Commerce Technology: Back to a Prominent Future

Kwei-Jay Lin — 2008-06-30T07:32:29-00:00

Internet Computing, IEEE, Vol. 12, No. 1. (2008), pp. 60-65.

E-commerce is a big business with a growing market size and has been a major driving force in the IT industry for the past decade. Companies now need to provide online shopping or marketing Web presence to allow for direct customer connections. In this article, the author reviews some primary e-commerce technologies, including auctions, negotiation, recommender systems, automated shopping, and trading. This paper also looks at how Web 2.0 provides new e-commerce opportunities.

Evolution of e-commerce Web sites: A conceptual framework and a longitudinal study

Sung-Chi Chu — 2007-02-23T13:45:50-00:00

Information & Management, Vol. 44, No. 2. (March 2007), pp. 154-164.

Before the 1990s, the digital exchange of information between companies was achieved using electronic data interchange (EDI) and needed agreement between the organizations. The early 1990s saw the commercialization of the Internet and the advent of open computer technology and connectivity became affordable for individuals as well as businesses. The consequence was the World Wide Web. As e-commerce activities extended across businesses, enterprises, and industries, a genre of Web sites emerged allowing the integrative management of business operations. Here, we provide an evolutionary perspective of e-commerce Web sites. We posited that there have been four eras. To chart the evolution of e-commerce Web sites, a conceptual framework was developed to characterize such sites. Based on the framework, we conducted a longitudinal study between 1993 and 2001. The result showed that the proposed four eras were clearly discernible.

Efficient online mining of large databases

Fadila Bentayeb — 2008-07-01T13:35:49-00:00

Int. J. Bus. Inf. Syst., Vol. 2, No. 3. (January 2007), pp. 328-350.

E&\#45;commerce practices in the Arabian Gulf GCC business culture&\#58; utilisation and outcomes patterns

Rafi Ashrafi — 2008-07-01T12:30:21-00:00

Int. J. Bus. Inf. Syst., Vol. 2, No. 4. (February 2007), pp. 351-371.

What makes patterns interesting in knowledge discovery systems

A Silberschatz — 2007-12-24T10:19:06-00:00

Transactions on Knowledge and Data Engineering, Vol. 8, No. 6. (1996), pp. 970-974.

One of the central problems in the field of knowledge discovery is the development of good measures of interestingness of discovered patterns. Such measures of interestingness are divided into objective measures-those that depend only on the structure of a pattern and the underlying data used in the discovery process, and the subjective measures-those that also depend on the class of users who examine the pattern. The focus of the paper is on studying subjective measures of interestingness. These measures are classified into actionable and unexpected, and the relationship between them is examined. The unexpected measure of interestingness is defined in terms of the belief system that the user has. Interestingness of a pattern is expressed in terms of how it affects the belief system. The paper also discusses how this unexpected measure of interestingness can be used in the discovery process

E-commerce partnering due diligence: A methodology for trust in e-commerce in food networks

Melanie Fritz — 2008-04-13T20:46:09-00:00

Food Economics - Acta Agriculturae Scandinavica, Section C, Vol. 4, No. 1. (2007), pp. 13-20.

Trust is one of the key facilitators for transactions in food networks. Recent developments in electronic transaction support such as e-commerce allow for efficiency improvements in exchange processes along food supply chains. However, the communication of trust between transaction partners is not sufficiently realized in existing e-commerce offers for food networks. To enable food networks to exploit efficiency potentials from electronic commerce, appropriate generation of trust and confidence at the transaction partners in the sense of an e-commerce partnering due diligence is necessary. This paper presents a methodology for a systematic identification of trust generation for electronic transactions in food networks. The methodology builds on three central elements: transaction decisions, the four phases of the transaction process, and the information and communication processes as mediating links. The transaction decision portfolio builds the central element of the methodology and contains criteria for the assessment of the reliability of transaction situations.

Analysis of recommendation algorithms for e-commerce

Badrul Sarwar — 2008-02-14T16:08:31-00:00

(2000), pp. 158-167.

Recommender systems apply statistical and knowledge discovery techniques to the problem of making product recommendations during a live customer interaction and they are achieving widespread success in E-Commerce nowadays. In this paper, we investigate several techniques for analyzing large-scale purchase and preference data for the purpose of producing useful recommendations to customers. In particular, we apply a collection of algorithms such as traditional data mining, nearest-neighbor...

E-Commerce Recommendation Applications

Ben Schafer — 2008-03-22T11:24:41-00:00

Data Mining and Knowledge Discovery, Vol. 5, No. 1/2. (2001), pp. 115-153.

Recommender systems are being used by an ever-increasing number of E-commerce sites to help consumers find products to purchase. What started as a novelty has turned into a serious business tool. Recommender systems use product knowledge -- either hand-coded knowledge provided by experts or "mined" knowledge learned from the behavior of consumers -- to guide consumers through the often-overwhelming task of locating products they will like. In this article we present an explanation of how...

Shiny happy people building trust?: photos on e-commerce websites and consumer trust

Jens Riegelsberger — 2005-10-09T23:52:04-00:00

(2003), pp. 121-128.

Lessons and Challenges from Mining Retail E-Commerce Data: Special Issue: Data Mining Lessons Learned (Guest Editors: Nada Lavrac, Hiroshi Motoda and Tom Fawcett)

Ron Kohavi — 2004-12-28T16:30:27-00:00

Machine Learning, Vol. 57, No. 1-2., 83.

Improving Customer Retention in E-Commerce through a Secure and Privacy-Enhanced Loyalty System

Matthias Enzmann — 2006-01-03T17:12:20-00:00

Information Systems Frontiers, Vol. 7, No. 4-5. (December 2005), pp. 359-370.

Dynamic Conversion Behavior at E-Commerce Sites

Wendy Moe — 2008-04-06T13:56:52-00:00

Manage. Sci., Vol. 50, No. 3. (March 2004), pp. 326-335.

Trusted Agent-Mediated E-Commerce Transaction Services via Digital Certificate Management

Yuh-Jong Hu — 2006-04-11T11:52:49-00:00

Electronic Commerce Research, Vol. 3, No. 3-4. (2003), pp. 221-243.

The Future of B2C E-Commerce

Siegfried Numberger — 2005-11-08T13:28:04-00:00

Electronic Markets, Vol. 15, No. 3. (2005), pp. 269-282.

The role of modeling in the performance testing of e-commerce applications

A Avritzer — 2006-08-17T05:28:14-00:00

Software Engineering, IEEE Transactions on, Vol. 30, No. 12. (2004), pp. 1072-1083.

An e-commerce scalability case study is presented in which both traditional performance testing and performance modeling were used to help tune the application for high performance. This involved the creation of a system simulation model as well as the development of an approach for test case generation and execution. We describe our experience using a simulation model to help diagnose production system problems, and discuss ways that the effectiveness of performance testing efforts was improved by its use.

Personalized e-commerce recommendations

P Markellou — 2007-01-11T10:44:29-00:00

e-Business Engineering, 2005. ICEBE 2005. IEEE International Conference on (2005), pp. 245-252.

Recommendation systems are special personalization tools that help users to find interesting information and services in complex online shops. Even though today's e-commerce environments have drastically evolved and now incorporate techniques from other domains and application areas such as Web mining, semantics, artificial intelligence, user modeling and profiling, etc. setting up a successful recommendation system is not a trivial or straightforward task. This paper argues that by monitoring, analyzing and understanding the behavior of customers, their demographics, opinions, preferences and history, as well as taking into consideration the specific e-shop ontology and by applying Web mining techniques, the effectiveness of produced recommendations can be significantly improved. In this way, the e-shop may upgrade users' interaction, increase its usability, convert users to buyers, retain current customers and establish long-term and loyal one-to-one relationships.

E-commerce and the retail process: a review

Steve Burt — 2008-05-22T23:23:09-00:00

Journal of Retailing and Consumer Services, Vol. 10, No. 5. (September 2003), pp. 275-286.

Views abound on the impact of the Internet and e-commerce on traditional forms of retailing. Scenarios range from on the one hand, the almost total devastation of existing physical retailing to, on the other, limited if any impact upon "real" retailing. Despite excessive hype, spectacular failures and the myriad of conflicting views and crystal-ball gazing, e-commerce processes and procedures provide the potential for a fundamental reassessment of how retailing operates and how retailers behave. Without doubt, the existing ways of operating and the associated cost structures within retailing will be reassessed under the onslaught of new technology and new retail structures. This paper reviews the published evidence on the impact of e-commerce on the retail process. It reviews the situation rather than introducing new evidence. The focus is on the process as it supports B2C activity and how retail processes and procedures could be affected by e-commerce, rather than a pre-occupation with sales impact through traditional merchandise and product sector typologies. Three conclusions are drawn. First, the largest retailers are now pursuing Internet-enabled advantages and cost reductions in operations, which could translate to an enhanced competitive position in process, structure and relationship terms. Secondly, consumer reactions to the new real and virtual offers will be fundamental to their success and failure, but as yet consumer reactions are not fully understood. Thirdly, existing retail floorspace will need enhancement in quality and presentation if it is to continue to provide retail functions.

Top 10 algorithms in data mining

Xindong Wu — 2008-02-01T20:11:32-00:00

Knowledge and Information Systems, Vol. 14, No. 1. (20 January 2008), pp. 1-37.

Abstract This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. These top 10 algorithms are among the most influential data mining algorithms in the research community. With each algorithm, we provide a description of the algorithm, discuss the impact of the algorithm, and review current and further research on the algorithm. These 10 algorithms cover classification, clustering, statistical learning, association analysis, and link mining, which are all among the most important topics in data mining research and development.

Information Retrieval on the World Wide Web

Venkat Gudivada — 2006-06-26T23:27:12-00:00

IEEE Internet Computing, Vol. 1, No. 5. (September 1997), pp. 58-68.

Web structure mining: an introduction

MG da Costa — 2008-03-07T02:30:22-00:00

Information Acquisition, 2005 IEEE International Conference on (2005), 6 pp..

Due to the increasing amount of data available online, the World Wide Web has becoming one of the most valuable resources for information retrievals and knowledge discoveries. Web mining technologies are the right solutions for knowledge discovery on the Web. The knowledge extracted from the Web can be used to raise the performances for Web information retrievals, question answering, and Web based data warehousing. In this paper, we provide an introduction of Web mining as well as a review of the Web mining categories. Then we focus on one of these categories: the Web structure mining. Within this category, we introduce link mining and review two popular methods applied in Web structure mining: HITS and PageRank.

Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data

Jaideep Srivastava — 2005-05-06T15:01:06-00:00

SIGKDD Explorations, Vol. 1, No. 2. (2000), pp. 12-23.

Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely preprocessing, pattern discovery, and pattern analysis. This paper describes each of these phases in detail. Given its application potential, Web usage mining has seen a rapid increase in interest, from both the research and practice communities. This paper provides a ...

Web mining research: a survey

Raymond Kosala — 2005-11-03T08:41:34-00:00

SIGKDD Explor. Newsl., Vol. 2, No. 1. (June 2000), pp. 1-15.

Scalable Web mining architecture for backward induction in data warehouse environment

Dongkwon Joo — 2006-02-23T09:15:02-00:00

Electrical and Electronic Technology, 2001. TENCON. Proceedings of IEEE Region 10 International Conference on, Vol. 1 (2001), pp. 8-10 vol.1.

For Web mining, the biggest problem is the scarcity of data. To overcome the problem and prepare as much needed data as possible for business intelligent information, we propose backward induction in Web mining. Web mining itself is an iterative process where data mining techniques are used back and forth and iteratively. To support backward induction and Web mining characteristics, the scalable Web mining architecture in a data warehouse environment is proposed. The proposed Web mining architecture has three kinds of scalabilities. These are: the scalabilities of operational database, the scalabilities of data model and the scalabilities of data mining engines. By implementing the scalable Web mining architecture with three kinds of scalabilities in a data warehouse environment to support backward induction procedures, we can extract business intelligent information from Web mining

Web mining: research and practice

P Kolari — 2007-11-19T15:57:15-00:00

Computing in Science & Engineering, Vol. 06, No. 4. (2004), pp. 49-53.

Web mining techniques seek to extract knowledge from Web data. This article provides an overview of past and current work in the three main areas of Web mining research - content, structure, and usage - as well as emerging work in semantic Web mining.

Mining the Web: Analysis of Hypertext and Semi Structured Data

Soumen Chakrabarti — 2005-03-02T15:59:19-00:00

(15 August 2002)

Mining the Web: Discovering Knowledge from Hypertext Data is the first book devoted entirely to techniques for producing knowledge from the vast body of unstructured Web data. Building on an initial survey of infrastructural issuesincluding Web crawling and indexingChakrabarti examines low-level machine learning techniques as they relate specifically to the challenges of Web mining. He then devotes the final part of the book to applications that unite infrastructure and analysis to bring machine learning to bear on systematically acquired and stored data. Here the focus is on results: the strengths and weaknesses of these applications, along with their potential as foundations for further progress. From Chakrabarti's workpainstaking, critical, and forward-lookingreaders will gain the theoretical and practical understanding they need to contribute to the Web mining effort. * A comprehensive, critical exploration of statistics-based attempts to make sense of Web Mining. * Details the special challenges associated with analyzing unstructured and semi-structured data. * Looks at how classical Information Retrieval techniques have been modified for use with Web data. * Focuses on today's dominant learning methods: clustering and classification, hyperlink analysis, and supervised and semi-supervised learning. * Analyzes current applications for resource discovery and social network analysis. * An excellent way to introduce students to especially vital applications of data mining and machine learning technology.</li></ul>

Web Mining

Johannes Furnkranz — 2005-05-06T15:35:35-00:00

The World-Wide Web provides every internet citizen with access to an abundance of information, but it becomes increasingly difficult to identify the relevant pieces of information. Research in web mining tries to address this problem by applying techniques from data mining and machine learning to Web data and documents. This chapter provides a brief overview of web mining techniques and research areas, most notably hypertext classification, wrapper induction, recommender systems and web usage...

The Past, Present, and Future for Software Architecture

P Kruchten — 2006-04-07T13:42:32-00:00

Software, IEEE, Vol. 23, No. 2. (2006), pp. 22-30.

It's been 10 years since David Garlan and Mary Shaw wrote their seminal book Software Architecture Perspective on an Emerging Discipline, since Maarten Boasson edited a special issue of IEEE Software on software architecture, and since the first International Software Architecture Workshop took place. What has happened over these 10 years? What have we learned? Where do we look for information? What's the community around this discipline? And where are we going from here?This article is part of a focus section on software architecture.

Semantic Web Mining: State of the art and future directions

Gerd Stumme — 2006-07-12T14:44:50-00:00

Web Semantics: Science, Services and Agents on the World Wide Web, Vol. 4, No. 2. (June 2006), pp. 124-143.

Semantic Web Mining aims at combining the two fast-developing research areas Semantic Web and Web Mining. This survey analyzes the convergence of trends from both areas: More and more researchers are working on improving the results of Web Mining by exploiting semantic structures in the Web, and they make use of Web Mining techniques for building the Semantic Web. Last but not least, these techniques can be used for mining the Semantic Web itself.The Semantic Web is the second-generation WWW, enriched by machine-processable information which supports the user in his tasks. Given the enormous size even of today's Web, it is impossible to manually enrich all of these resources. Therefore, automated schemes for learning the relevant information are increasingly being used. Web Mining aims at discovering insights about the meaning of Web resources and their usage. Given the primarily syntactical nature of the data being mined, the discovery of meaning is impossible based on these data only. Therefore, formalizations of the semantics of Web sites and navigation behavior are becoming more and more common. Furthermore, mining the Semantic Web itself is another upcoming application. We argue that the two areas Web Mining and Semantic Web need each other to fulfill their goals, but that the full potential of this convergence is not yet realized. This paper gives an overview of where the two areas meet today, and sketches ways of how a closer integration could be profitable.

MapReduce: Simplified Data Processing on Large Clusters

Jeffrey Dean — 2005-12-08T17:08:27-00:00

OSDI '04, pp. 137-150.

MapReduce is a programming model and an associated implementation for processing and generating large data sets. Users specify a _map_ function that processes a key/value pair to generate a set of intermediate key/value pairs, and a _reduce_ function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model, as shown in the paper. Programs written in this functional style are automatically parallelized and executed on a large cluster of commodity machines. The run-time system takes care of the details of partitioning the input data, scheduling the program's execution across a set of machines, handling machine failures, and managing the required inter- machine communication. This allows programmers without any experience with parallel and distributed systems to easily utilize the resources of a large distributed system. Our implementation of MapReduce runs on a large cluster of commodity machines and is highly scalable: a typical MapReduce computation processes many terabytes of data on thousands of machines. Programmers find the system easy to use: hundreds of MapReduce programs have been implemented and upwards of one thousand MapReduce jobs are executed on Google's clusters every day.

The anatomy of a large-scale hypertextual Web search engine

Sergey Brin — 2004-11-22T17:49:28-00:00

Computer Networks and ISDN Systems, Vol. 30, No. 1--7. (1998), pp. 107-117.

In this paper, we present Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems. The prototype with a full text and hyperlink database of at least 24 million pages is available at

How Google is changing medicine

Dean Giustini — 2005-12-28T11:19:16-00:00

BMJ, Vol. 331, No. 7531. (24 December 2005), pp. 1487-1488.

Automatic Meaning Discovery Using Google

Rudi Cilibrasi — 2004-12-22T12:39:20-00:00

(21 December 2004)

We propose a new method to extract semantic knowledge from the world-wide-web for both supervised and unsupervised learning using the Google search engine in an unconventional manner. The approach is novel in its unrestricted problem domain, simplicity of implementation, and manifestly ontological underpinnings. We give evidence of elementary learning of the semantics of concepts, in contrast to most prior approaches. The method works as follows: The world-wide-web is the largest database on earth, and it induces a probability mass function, the Google distribution, via page counts for combinations of search queries. This distribution allows us to tap the latent semantic knowledge on the web. Shannon's coding theorem is used to establish a code-length associated with each search query. Viewing this mapping as a data compressor, we connect to earlier work on Normalized Compression Distance. We give applications in (i) unsupervised hierarchical clustering, demonstrating the ability to distinguish between colors and numbers, and to distinguish between 17th century Dutch painters; (ii) supervised concept-learning by example, using Support Vector Machines, demonstrating the ability to understand electrical terms, religious terms, emergency incidents, and by conducting a massive experiment in understanding WordNet categories; and (iii) matching of meaning, in an example of automatic English-Spanish translation.

Google Analytics

Mary Tyler — 2007-08-17T09:34:42-00:00

(12 September 2006)

You know you need to analyze the success of your Web site, but how? Do you even know what to look for? Is there a tool powerful enough to help you evaluate your marketing efforts, products, and services, but simple enough to use if you're not a propeller-head? Google Analytics is that tool, and this is the handbook you need to make it work for you. Learn to set up Google Analytics, understand the reports it generates, and use the information to make your Web site a real asset to your business. <ul> <li> Get familiar with the concept of analytics, what Google Analytics offers, and how it compares to popular site statistics programs. <li> Learn to set up the program, navigate the interface, understand filters, and use goal-setting features. <li> Integrate Google Analytics with Google AdWords. <li> Make the most of reporting dashboards. <li> Find out how to use analytics for marketing and content optimization. <li> Understand what each type of report means and how to interpret it. <li> Explore how other companies have used analytics to improve site performance. <li> Investigate how to use Google Analytics for complete e-commerce analysis. </ul> Order your copy today and make your Web site work for you!

Recommending collaboration with social networks: a comparative evaluation

David Mcdonald — 2005-04-19T22:33:37-00:00

(2003), pp. 593-600.

Efficient Monitoring Algorithm for Fast News Alerts

Ka Sia — 2007-10-17T13:17:21-00:00

Knowledge and Data Engineering, IEEE Transactions on, Vol. 17, No. 7. (2007), pp. 950-961.

Recently, there has been a dramatic increase in the use of XML data to deliver information over the Web. Personal Weblogs, news Web sites, and discussion forums are now publishing RSS feeds for their subscribers to retrieve new postings. As the popularity of personal Weblogs and RSS feeds grows rapidly, RSS aggregation services and blog search engines have appeared, which try to provide a central access point for simpler access and discovery of new content from a large number of diverse RSS sources. In this paper, we study how the RSS aggregation services should monitor the data sources to retrieve new content quickly using minimal resources and to provide its subscribers with fast news alerts. We believe that the change characteristics of RSS sources and the general user access behavior pose distinct requirements that make this task significantly different from the traditional index refresh problem for Web search engines. Our studies on a collection of 10,000 RSS feeds reveal some general characteristics of the RSS feeds and show that, with proper resource allocation and scheduling, the RSS aggregator provides news alerts significantly faster than the best existing approach.

Applying collaborative filtering techniques to movie search for better ranking and browsing

Seung-Taek Park — 2007-10-31T21:40:11-00:00

(2007), pp. 550-559.

Effective personalization based on association rule discovery from web usage data

Bamshad Mobasher — 2007-11-20T00:26:02-00:00

(2001), pp. 9-15.