bluuebunny/crossref_metadata_2025 huggingface dataset:
Multi-Instance Learning with MultiObjective Genetic Programming
Authors: Zafra Amelia
Published: May 2011

The multiple-instance problem is a difficult machine learning problem that appears in cases where knowledge about training examples is incomplete. In this problem, the teacher labels examples that are sets (also called bags) of instances. The teacher does not label whether an individual instance in a bag is positive or negative. The learning algorithm needs to generate a classifier that will correctly classify unseen examples (i.e., bags of instances). This learning framework is receiving growing attention in the machine learning community and since it was introduced by Dietterich, Lathrop, Lozano-Perez (1997), a wide range of tasks have been formulated as multi-instance problems. Among these tasks, we can cite content-based image retrieval (Chen, Bi, & Wang, 2006) and annotation (Qi and Han, 2007), text categorization (Andrews, Tsochantaridis, & Hofmann, 2002), web index page recommendation (Zhou, Jiang, & Li, 2005; Xue, Han, Jiang, & Zhou, 2007) and drug activity prediction (Dietterich et al., 1997; Zhou & Zhang, 2007). In this chapter we introduce MOG3P-MI, a multiobjective grammar guided genetic programming algorithm to handle multi-instance problems. In this algorithm, based on SPEA2, individuals represent classification rules which make it possible to determine if a bag is positive or negative. The quality of each individual is evaluated according to two quality indexes: sensitivity and specificity. Both these measures have been adapted to MIL circumstances. Computational experiments show that the MOG3P-MI is a robust algorithm for classification in different domains where achieves competitive results and obtain classifiers which contain simple rules which add comprehensibility and simplicity in the knowledge discovery process, being suitable method for solving MIL problems (Zafra & Ventura, 2007).
A Novel Approach on Negative Association Rules
Authors: Kouris Ioannis N.
Published: May 2011

Research in association rules mining has initially concentrated in solving the obvious problem of finding positive association rules; that is rules among items that exist in the stored transactions. It was only several years after that the possibility of finding also negative association rules became especially appealing and was investigated. Nevertheless researchers based their assumptions regarding negative association rules on the absence of items from transactions. This assumption though besides being dubious, since it equated the absence of an item with a conflict or negative effect on the rest items, it also brought out a series of computational problems with the amount of possible patterns that had to be examined and analyzed. In this work we give an overview of the works having engaged with the subject until now and present a novel view for the definition of negative influence among items.
Offline Signature Recognition
Authors: Chakravarty Indrani
Published: May 2011

The most commonly used protection mechanisms today are based on either what a person possesses (e.g. an ID card) or what the person remembers (like passwords and PIN numbers). However, there is always a risk of passwords being cracked by unauthenticated users and ID cards being stolen, in addition to shortcomings like forgotten passwords and lost ID cards (Huang & Yan, 1997). To avoid such inconveniences, one may opt for the new methodology of Biometrics, which though expensive will be almost infallible as it uses some unique physiological and/or behavioral (Huang & Yan, 1997) characteristics possessed by an individual for identity verification. Examples include signature, iris, face, and fingerprint recognition based systems. The most widespread and legally accepted biometric among the ones mentioned, especially in the monetary transactions related identity verification areas is carried out through handwritten signatures, which belong to behavioral biometrics (Huang & Yan,1997). This technique, referred to as signature verification, can be classified into two broad categories - online and off-line. While online deals with both static (for example: number of black pixels, length and height of the signature) and dynamic features (such as acceleration and velocity of signing, pen tilt, pressure applied) for verification, the latter extracts and utilizes only the static features (Ramesh and Murty, 1999). Consequently, online is much more efficient in terms of accuracy of detection as well as time than off-line. But, since online methods are quite expensive to implement, and also because many other applications still require the use of off-line verification methods, the latter, though less effective, is still used in many institutions.
Path Mining and Process Mining for Workflow Management Systems
Authors: Cardoso Jorge , van der Aalst W.M.P.
Published: May 2011

Business process management systems (Smith and Fingar 2003) provide a fundamental infrastructure to define and manage business processes and workflows. These systems are often called process aware information systems (Dumas, Aalst et al. 2005) since they coordinate the automation of interconnected tasks. Well-known systems include Tibco, WebSphere MQ Workflow, FileNet, COSA, etc. Other types of systems, such as ERP, CRM, SCM, and B2B, are also driven by explicit process models and are configured on the basis of a workflow model specifying the order in which tasks need to be executed. When process models or workflows are executed, the underlying management system generates data describing the activities being carried out which is stored in a log file. This log of data can be used to discover and extract knowledge about the execution and structure of processes. The goal of process mining is to extract information about processes from logs. When observing recent developments with respect to process aware information systems (Dumas, Aalst et al. 2005) three trends can be identified. First of all, workflow technology is being embedded in service oriented architectures. Second, there is a trend towards providing more flexibility. It is obvious that in the end business processes interface with people. Traditional workflow solutions expect the people to adapt to the system. However, it is clear that in many situations this is not acceptable. Therefore, systems are becoming more flexible and adaptable. The third trend is the omnipresence of event logs in today’s systems. Current systems ranging from cross-organizational systems to embedded systems provide detailed event logs. In a service oriented architecture events can be monitored in various ways. Moreover, physical devices start to record events. Already today many professional systems (X-ray machines, wafer stepper, high-end copiers, etc.) are connected to the internet. For example, Philips Medical Systems is able to monitor all events taking place in their X-ray machines. The three trends mentioned above are important enablers for path mining and process mining. The abundance of recorded events in structured format is an important enabler for the analysis of run-time behavior. Moreover, the desire to be flexible and adaptable also triggers the need for monitoring. If processes are not enforced by some system, it is relevant to find out what is actually happening, e.g., how frequently do people deviate from the default procedure.
The Personal Name Problem and a Data Mining Solution
Authors: Phua Clifton , Lee Vincent , Smith-Miles Kate
Published: May 2011

Almost every person has a life-long personal name which is officially recognised and has only one correct version in their language. Each personal name typically has two components/parts: a first name (also known as given, fore, or Christian name) and a last name (also known as family name or surname). Both these name components are strongly influenced by cultural, economic, historical, political, and social backgrounds. In most cases, each of these two components can have more than a single word and the first name is usually gender-specific. (see Figure 1). There are three important practical considerations for personal name analysis: • Balance between manual checking and analytical computing. Intuitively, a small proportion of names should be manually reviewed, the result has to be reasonably accurate, and each personal name should not take too long to be processed. • Reliability of the verification data has to be examined. By keeping the name verification database’s updating process separate from incoming names, it can prevent possible data manipulation/corruption over time. However, the incompatibility of names in databases can also be caused by genuine reasons as such as cultural and historical traditions, translation and transliteration, reporting and recording variations, and typographical and phonetic errors (Borgman and Siegfried, 1992).
Physical Data Warehousing Design
Authors: Bellatreche Ladjel , Mohania Mukesh
Published: May 2011

Recently, organizations have increasingly emphasized applications in which current and historical data are analyzed and explored comprehensively, identifying useful trends and creating summaries of the data in order to support high-level decision making. Every organization keeps accumulating data from different functional units, so that they can be analyzed (after integration), and important decisions can be made from the analytical results. Conceptually, a data warehouse is extremely simple. As popularized by Inmon (1992), it is a “subject-oriented, integrated, time-invariant, nonupdatable collection of data used to support management decision-making processes and business intelligence”. A data warehouse is a repository into which are placed all data relevant to the management of an organization and from which emerge the information and knowledge needed to effectively manage the organization. This management can be done using data-mining techniques, comparisons of historical data, and trend analysis. For such analysis, it is vital that (1) data should be accurate, complete, consistent, well defined, and time-stamped for informational purposes; and (2) data should follow business rules and satisfy integrity constraints. Designing a data warehouse is a lengthy, time-consuming, and iterative process. Due to the interactive nature of a data warehouse application, having fast query response time is a critical performance goal. Therefore, the physical design of a warehouse gets the lion’s part of research done in the data warehousing area. Several techniques have been developed to meet the performance requirement of such an application, including materialized views, indexing techniques, partitioning and parallel processing, and so forth. Next, we briefly outline the architecture of a data warehousing system.
Predicting Resource Usage for Capital Efficient Marketing
Authors: Mani D. R. , Betz Andrew L. , Drew James H.
Published: May 2011

A structural conflict exists in businesses which sell services whose production costs are discontinuous and whose consumption is continuous but variable. A classic example is in businesses where capital-intensive infrastructure is necessary for provisioning service, but the capacity resulting from capital outlay is not always fully and efficiently utilized. Marketing departments focus on initiatives which increase infrastructure usage to improve both customer retention and on-going revenue. Engineering and operations departments focus on the cost of service provision to improve the capital efficiency of revenue dollar received. Consequently, a marketing initiative to increase infrastructure usage may be resisted by engineering if its introduction would require great capital expense to accommodate that increased usage. This conflict is exacerbated when a usage-enhancing initiative tends to increase usage variability so that capital expenditures are triggered with only small increases in total usage. A data warehouse whose contents encompass both these organizational functions has the potential to mediate this conflict, and data mining can be the tool for this mediation. Marketing databases typically have customer data on rate plans, usage and past response to marketing promotions. Engineering databases generally record infrastructure locations, usages and capacities. Other information is often available from both general domains to allow for the aggregation, or clustering of customer types, rate plans and marketing promotions so that marketing proposals and their consequences can be systematically evaluated to aid in decision making. These databases generally contain such voluminous or complicated data that classical data analysis tools are inadequate. In this chapter, we look at a case study where data mining is applied to predicting capital-intensive resource or infrastructure usage, with the goal of guiding marketing decisions to enable capital efficient marketing. Although the data mining models developed in this chapter do not provide conclusive positions on specific marketing initiatives and their engineering consequences, the usage revenues and infrastructure performance predicted by these models provide systematic, sound, and quantitative input for making balanced and cost-effective business decisions.
Reasoning about Frequent Patterns with Negation
Authors: Kryszkiewicz Marzena
Published: May 2011

Discovering of frequent patterns in large databases is an important data mining problem. The problem was introduced in (Agrawal, Imielinski & Swami, 1993) for a sales transaction database. Frequent patterns were defined there as sets of items that are purchased together frequently. Frequent patterns are commonly used for building association rules. For example, an association rule may state that 80% of customers who buy fish also buy white wine. This rule is derivable from the fact that fish occurs in 5% of sales transactions and set {fish, white wine} occurs in 4% of transactions. Patterns and association rules can be generalized by admitting negation. A sample association rule with negation could state that 75% of customers who buy coke also buy chips and neither beer nor milk. The knowledge of this kind is important not only for sales managers, but also in medical areas (Tsumoto, 2002). Admitting negation in patterns usually results in an abundance of mined patterns, which makes analysis of the discovered knowledge infeasible. It is thus preferable to discover and store a possibly small fraction of patterns, from which one can derive all other significant patterns when required. In this chapter, we introduce first lossless representations of frequent patterns with negation.
Seamless Structured Knowledge Acquisition
Authors: Parpola Päivikki
Published: May 2011

Some parts of this text, namely “Co-operative Building, Adaptation, and Evolution of Abstract Models of a KB” and most subsections in “Performing Reasoning in SOOKAT According to a KB”, have appeared in an article (DOI:10.1007/s10115-004-0181-6) published in the ‘Knowledge And Information Systems’ journal (Parpola, 2004). A knowledge base (KB) contains data and instructions for using it (e.g., as a rule base). A KB containing knowledge possessed by experts can be used in an expert system. It can solve problems requiring expert knowledge, explain its decisions and deal with uncertainty. An expert system can be used as a basis for a larger system, called a knowledge-based system (KBS). Knowledge acquisition (KA) that is the development and maintenance of KBs, (e.g. an expert system), can be divided into several phases, performed sequentially and iteratively. Some phases may be performed in parallel with other phases. The most commonly recognised phases are requirements definition, analysis, design, and implementation. Disintegration, or the gap between phases of development, especially between abstract and executable descriptions, was recognised during the early stages of KA (Marcus, 1988a; Motta, Rajan and Eisenstadt, 1988). It complicates the development of KBs and hinders traceability between parts of abstract and executable descriptions.
Search Engines and their Impact on Data Warehouses
Authors: Peter Hadrian
Published: May 2011

Over the past ten years or so data warehousing has emerged as a new technology in the database environment. “A data warehouse is a global repository that stores pre-processed queries on data which resides in multiple, possibly heterogeneous, operational or legacy sources” (Samtani et al, 2004). Data warehousing as a specialized field is continuing to grow and mature. Despite the phenomenal upgrades in terms of data storage capability there has been a flood of new streams of data entering the warehouse. During the last decade there has been an increase from 1 terabyte to 100 terabyte and, soon to be 1 petabyte, environments. Therefore, the ability to search, mine and analyze data of such immense proportions remains a significant issue even as analytical capabilities increase. The data warehouse is an environment which is readily tuned to maximize the efficiency of making useful decisions. However the advent of commercial uses of the Internet on a large scale has opened new possibilities for data capture and integration into the warehouse. While most of the data necessary for a data warehouse originates from the organization’s internal (operational) data sources, additional data is available externally that can add significant value to the data warehouse. One of the major reasons why organizations implement data warehousing is to make it easier, on a regular basis, to query and report data from multiple transaction processing systems and/or from external sources. One important source of this external data is the Internet. A few researchers (Walters, 1997; Strand & Olsson, 2004; Strand & Wangler, 2004) have investigated the possibility of incorporating external data in data warehouses, however, there is little literature detailing research in which the Internet is the source of the external data. In (Peter & Greenidge, 2005) a high-level model, the Data Warehousing Search Engine (DWSE), was presented. However, in this article we examine in some detail the issues in search engine technology that make the Internet a plausible and reliable source for external data. As John Ladley (Ladley, 2005) states “There is a new generation of Data Warehousing on the horizon that reflects maturing technology and attitudes”. Our long-term goal is to design this new generation Data Warehouse.
Semi-Structured Document Classification
Authors: Denoyer Ludovic
Published: May 2011

Document classification developed over the last ten years, using techniques originating from the pattern recognition and machine learning communities. All these methods do operate on flat text representations where word occurrences are considered independents. The recent paper (Sebastiani, 2002) gives a very good survey on textual document classification. With the development of structured textual and multimedia documents, and with the increasing importance of structured document formats like XML, the document nature is changing. Structured documents usually have a much richer representation than flat ones. They have a logical structure. They are often composed of heterogeneous information sources (e.g. text, image, video, metadata, etc). Another major change with structured documents is the possibility to access document elements or fragments. The development of classifiers for structured content is a new challenge for the machine learning and IR communities. A classifier for structured documents should be able to make use of the different content information sources present in an XML document and to classify both full documents and document parts. It should easily adapt to a variety of different sources (e.g. to different Document Type Definitions). It should be able to scale with large document collections.
Semi-Supervised Learning
Authors: Scheffer Tobias
Published: May 2011

For many classification problems, unlabeled training data are inexpensive and readily available, whereas labeling training data imposes costs. Semi-supervised classification algorithms aim at utilizing information contained in unlabeled data in addition to the (few) labeled data. Semi-supervised (for an example, see Seeger, 2001) has a long tradition in statistics (Cooper & Freeman, 1970); much early work has focused on Bayesian discrimination of Gaussians. The Expectation Maximization (EM) algorithm (Dempster, Laird, & Rubin, 1977) is the most popular method for learning generative models from labeled and unlabeled data. Model-based, generative learning algorithms find model parameters (e.g., the parameters of a Gaussian mixture model) that best explain the available labeled and unlabeled data, and they derive the discriminating classification hypothesis from this model. In discriminative learning, unlabeled data is typically incorporated via the integration of some model assumption into the discriminative framework (Miller & Uyar, 1997; Titterington, Smith, & Makov, 1985). The Transductive Support Vector Machine (Vapnik, 1998; Joachims, 1999) uses unlabeled data to identify a hyperplane that has a large distance not only from the labeled data but also from all unlabeled data. This identification results in a bias toward placing the hyperplane in regions of low density p(x). Recently, studies have covered graph-based approaches that rely on the assumption that neighboring instances are more likely to belong to the same class than remote instances (Blum & Chawla, 2001). A distinct approach to utilizing unlabeled data has been proposed by de Sa (1994), Yarowsky (1995) and Blum and Mitchell (1998). When the available attributes can be split into independent and compatible subsets, then multi-view learning algorithms can be employed. Multi-view algorithms, such as co-training (Blum & Mitchell, 1998) and co-EM (Nigam & Ghani, 2000), learn two independent hypotheses, which bootstrap by providing each other with labels for the unlabeled data. An analysis of why training two independent hypotheses that provide each other with conjectured class labels for unlabeled data might be better than EM-like self-training has been provided by Dasgupta, Littman, and McAllester (2001) and has been simplified by Abney (2002). The disagreement rate of two independent hypotheses is an upper bound on the error rate of either hypothesis. Multi-view algorithms minimize the disagreement rate between the peer hypotheses (a situation that is most apparent for the algorithm of Collins & Singer, 1999) and thereby the error rate. Semi-supervised learning is related to active learning. Active learning algorithms are able to actively query the class labels of unlabeled data. By contrast, semi-supervised algorithms are bound to learn from the given data.
Storage Systems for Data Warehousing
Authors: Thomasian Alexander
Published: May 2011

Data storage requirements have consistently increased over time. According to the latest WinterCorp survey (http://www/WinterCorp.com), “The size of the world’s largest databases has tripled every two years since 2001.” With database size in excess of 1 terabyte, there is a clear need for storage systems that are both cost effective and highly reliable. Historically, large databases are implemented on mainframe systems. These systems are large and expensive to purchase and maintain. In recent years, large data warehouse applications are being deployed on Linux and Windows hosts, as replacements for the existing mainframe systems. These systems are significantly less expensive to purchase while requiring less resources to run and maintain. With large databases it is less feasible, and less cost effective, to use tapes for backup and restore. The time required to copy terabytes of data from a database to a serial medium (streaming tape) is measured in hours, which would significantly degrade performance and decreases availability. Alternatives to serial backup include local replication, mirroring, or geoplexing of data. The increasing demands of larger databases must be met by less expensive disk storage systems, which are yet highly reliable and less susceptible to data loss. This article is organized into five sections. The first section provides background information that serves to introduce the concepts of disk arrays. The following three sections detail the concepts used to build complex storage systems. The focus of these sections is to detail: (i) Redundant Arrays of Independent Disks (RAID) arrays; (ii) multilevel RAID (MRAID); (iii) concurrency control and storage transactions. The conclusion contains a brief survey of modular storage prototypes.
Supporting Imprecision in Database Systems
Authors: Nambiar Ullas
Published: May 2011

A query against incomplete or imprecise data in a database1, or a query whose search conditions are imprecise can both result in answers that do not satisfy the query completely. Such queries can be broadly termed as imprecise queries. Today’s database systems are designed largely for precise queries against a database of precise and complete data. Range queries (e.g., Age BETWEEN 20 AND 30) and disjunctive queries (e.g., Name=“G. W. Bush” OR Name=“George Bush”) do allow for some imprecision in queries. However, these extensions to precise queries are unable to completely capture the expressiveness of an imprecise query. Supporting imprecise queries (e.g., Model like “Camry” and Price around “$15000”) over databases necessitates a system that integrates a similarity search paradigm over structured and semi-structured data. Today’s relational database systems, as they are designed to support precise queries against precise data, use such precise access support mechanisms as indexing, hashing, and sorting. Such mechanisms are used for fast selective searches of records within a table and for joining two tables based on precise matching of values in join fields in the tables. The imprecise nature of the search conditions in queries will make such access mechanisms largely useless. Thus, supporting imprecise queries over existing databases would require adding support for imprecision within the query engine and meta-data management schemes like indexes. Extending a database to support imprecise queries would involve changing the query processing and data storage models being used by the database. But, the fact that databases are generally used by other applications and therefore must retain their behaviour could become a key inhibitor to any technique that relies on modifying the database to enable support for imprecision. For example, changing an airline reservation database will necessitate changes to other connected systems including travel agency databases, partner airline databases etc. Even if the database is modifiable, we would still require a domain expert and/or end user to provide the necessary distance metrics and domain ontology. Domain ontologies do not exist for all possible domains and the ones that are available are far from being complete. Therefore, a feasible solution for answering imprecise queries should neither assume the ability to modify the properties of the database nor require users (both lay and expert) to provide much domain specific information.
Temporal Event Sequence Rule Mining
Authors: Harms Sherri K.
Published: May 2011

The emergence of remote sensing, scientific simulation and other survey technologies has dramatically enhanced our capabilities to collect temporal data. However, the explosive growth in data makes the management, analysis, and use of data both difficult and expensive. Methods that characterize interesting or unusual patterns from the volumes of temporal data are needed (Roddick & Spiliopoulou, 2002; Han & Kamber, 2005). The association rule mining methods described in this chapter provide the ability to find periodic occurrences of inter-sequential factors of interest, from groups of long, non-transactional temporal event sequences. Association rule mining is well-known to work well for problems related to the recognition of frequent patterns of data (Han & Kamber, 2005). Rules are relatively easy for humans to interpret and have a long history of use in artificial intelligence for representing knowledge learned from data.
Text Mining for Business Intelligence
Authors: Markellos Konstantinos
Published: May 2011

Nowadays, business executives understand that timely and accurate knowledge has become crucial factor for making better and faster business decisions and providing in this way companies a competitive advantage. Especially, with the vast majority of corporate information stored as text in various databases, the need to efficiently extract actionable knowledge from these assets is growing rapidly. Existing approaches are incapable of handling the constantly increasing volumes of textual data and only a small percentage can be effectively analyzed. Business Intelligence (BI) provides a broad set of techniques, tools and technologies that facilitate management of business knowledge, performance, and strategy through automated analytics or human-computer interaction. It unlocks the “hidden” knowledge of the data and enables companies to gain insight into better customers, markets, and business information by combing through vast quantities of data quickly, thoroughly and with sharp analytical precision. A critical component that impacts business performance relates to the evaluation of competition. Measurement and assessment of technological and scientific innovation and the production of relative indicators can provide a clear view about progress. Information related to those activities is usually stored to large databases and can be distinguished in: research information stored in publications or scientific magazines and developmentproduction information stored in patents. Patents are closely related to Technology Watch, the activity of surveying the development of new technologies, of new products, of tendencies of technology as well as measuring their impact on actual technologies, organizations or people. Statistical exploitation of patent data may lead to useful conclusions about technological development, trends or innovation (Chappelier et al., 2002). Traditional methods of extracting knowledge from patent databases are based on manual analysis carried out by experts. Nowadays, these methods are impractical as patent databases grow exponentially. Text Mining (TM) therefore corresponds to the extension of the more traditional Data Mining approach to unstructured textual data and is primarily concerned with the extraction of information implicitly contained in collections of documents. The use of automatic analysis techniques allows us to valorize in a more efficient way the potential wealth of information that the textual databases represent (Hotho et al., 2005). This article describes a methodological approach and an implemented system that combines efficient TM techniques and tools. The BI platform enables users to access, query, analyze, and report the patents. Moreover, future trends and challenges are illustrated and some new research that we are pursuing to enhance the approach are discussed.
Text Mining Methods for Hierarchical Document Indexing
Authors: Kim Han-Joon
Published: May 2011

We have recently seen a tremendous growth in the volume of online text documents from networked resources such as the Internet, digital libraries, and company-wide intranets. One of the most common and successful methods of organizing such huge amounts of documents is to hierarchically categorize documents according to topic (Agrawal, Bayardo & Srikant, 2000; Kim & Lee, 2003). The documents indexed according to a hierarchical structure (termed ‘topic hierarchy’ or ‘taxonomy’) are kept in internal categories as well as in leaf categories, in the sense that documents at a lower category have increasing specificity. Through the use of a topic hierarchy, users can quickly navigate to any portion of a document collection without being overwhelmed by a large document space. As is evident from the popularity of web directories such as Yahoo (http:// www.yahoo.com/) and Open Directory Project (http:// www.dmoz.org/), topic hierarchies have increased in importance as a tool for organizing or browsing a large volume of electronic text documents. Currently, the topic hierarchies maintained by most information systems are manually constructed and maintained by human editors. The topic hierarchy should be continuously subdivided to cope with the high rate of increase in the number of electronic documents. For example, the topic hierarchy of the Open Directory Project has now reached about 590,000 categories. However, manually maintaining the hierarchical structure incurs several problems. First, such a manual task is prohibitively costly as well as time-consuming. Until now, large search portals such as Yahoo have invested significant time and money into maintaining their taxonomy, but obviously they will not be able to keep up with the pace of growth and change in electronic documents through such manual activity. Moreover, for a dynamic networked resource (e.g., World Wide Web) that contains highly heterogeneous documents accompanied by frequent content changes, maintain- ing a ‘good’ hierarchy is fraught with difficulty, and oftentimes is beyond the human experts’ capabilities. Lastly, since human editors’ categorization decision is not only highly subjective but their subjectivity is also variable over time, it is difficult to maintain a reliable and consistent hierarchical structure. The above limitations require information systems that can provide intelligent organization capabilities with topic hierarchies. Related commercial systems include Verity Knowledge Organizer (http://www.verity.com/), Inktomi Directory Engine (http://www.inktomi.com/), and Inxight Categorizer (http://www.inxight.com/), which enable a browsable web directory to be automatically built. However, these systems did not address the (semi-)automatic evolving capabilities of organizational schemes and classification models at all. This is one of the reasons why the commercial taxonomy-based services do not tend to be as popular as their manually constructed counterparts, such as Yahoo.
Utilizing Fuzzy Decision Trees in Decision Making
Authors: Beynonm Malcolm J.
Published: May 2011

The seminal work of Zadeh (1965), namely fuzzy set theory (FST), has developed into a methodology fundamental to analysis that incorporates vagueness and ambiguity. With respect to the area of data mining, it endeavours to find potentially meaningful patterns from data (Hu & Tzeng, 2003). This includes the construction of if-then decision rule systems, which attempt a level of inherent interpretability to the antecedents and consequents identified for object classification (See Breiman, 2001). Within a fuzzy environment this is extended to allow a linguistic facet to the possible interpretation, examples including mining time series data (Chiang, Chow, & Wang, 2000) and multi-objective optimisation (Ishibuchi & Yamamoto, 2004). One approach to if-then rule construction has been through the use of decision trees (Quinlan, 1986), where the path down a branch of a decision tree (through a series of nodes), is associated with a single if-then rule. A key characteristic of the traditional decision tree analysis is that the antecedents described in the nodes are crisp, where this restriction is mitigated when operating in a fuzzy environment (Crockett, Bandar, Mclean, & O’Shea, 2006). This chapter investigates the use of fuzzy decision trees as an effective tool for data mining. Pertinent to data mining and decision making, Mitra, Konwar and Pal (2002) succinctly describe a most important feature of decision trees, crisp and fuzzy, which is their capability to break down a complex decision-making process into a collection of simpler decisions and thereby, providing an easily interpretable solution.
Variable Length Markov Chains for Web Usage Mining
Authors: Borges José , Levene Mark
Published: May 2011

Web usage mining is usually defined as the discipline that concentrates on developing techniques that model and study users’ Web navigation behavior by means of analyzing data obtained from user interactions with Web resources; see (Mobasher, 2006; Liu, 2007) for recent reviews on web usage mining. When users access Web resources they leave a trace behind that is stored in log files, such traces are called clickstream records. Clickstream records can be preprocessed into time-ordered sessions of sequential clicks (Spiliopoulou et al., 2003), where a user session represents a trail the user followed through the Web space. The process of session reconstruction is called sessionizing. Understanding user Web navigation behavior is a fundamental step in providing guidelines on how to improve users’ Web experience. In this context, a model able to represent usage data can be used to induce frequent navigation patterns, to predict future user navigation intentions, and to provide a platform for adapting Web pages according to user specific information needs (Anand et al., 2005; Eirinaki et al., 2007). Techniques using association rules (Herlocker et al., 2004) or clustering methods (Mobasher et al., 2002) have been used in this context. Given a set of transactions clustering techniques can be used, for example, to find user segments, and association rule techniques can be used, for example, to find important relationships among pages based on the users navigational patterns. These methods have the limitation that the ordering of page views is not taken into consideration in the modeling of user sessions (Liu, 2007). Two methods that take into account the page view ordering are: tree based methods (Chen et al., 2003) used for prefetching Web resources, and Markov models (Borges et al., 2000; Deshpande et al., 2004) used for link prediction. Moreover, recent studies have been conducted on the use of visualization techniques for discovering navigational trends from usage data (Chen et al., 2007a; Chen et al., 2007b).
Web Usage Mining with Web Logs
Authors: Huang Xiangji
Published: May 2011

With the rapid growth of the World Wide Web, the use of automated Web-mining techniques to discover useful and relevant information has become increasingly important. One challenging direction is Web usage mining, wherein one attempts to discover user navigation patterns of Web usage from Web access logs. Properly exploited, the information obtained from Web usage log can assist us to improve the design of a Web site, refine queries for effective Web search, and build personalized search engines. However, Web log data are usually large in size and extremely detailed, because they are likely to record every aspect of a user request to a Web server. It is thus of great importance to process the raw Web log data in an appropriate way, and identify the target information intelligently. In this chapter, we first briefly review the concept of Web Usage Mining and discuss its difference from classic Knowledge Discovery techniques, and then focus on exploiting Web log sessions, defined as a group of requests made by a single user for a single navigation purpose, in Web usage mining. We also compare some of the state-of-the-art techniques in identifying log sessions from Web servers, and present some popular Web mining techniques, including Association Rule Mining, Clustering, Classification, Collaborative Filtering, and Sequential Pattern Learning, that can be exploited on the Web log data for different research and application purposes.
Affective Computing
Authors: Pantic Maja
Published: May 2011

We seem to be entering an era of enhanced digital connectivity. Computers and Internet have become so embedded in the daily fabric of people’s lives that people simply cannot live without them (Hoffman, Novak, & Venkatesh, 2004). We use this technology to work, to communicate, to shop, to seek out new information, and to entertain ourselves. With this ever-increasing diffusion of computers in society, human–computer interaction (HCI) is becoming increasingly essential to our daily lives. HCI design was first dominated by direct manipulation and then delegation. The tacit assumption of both styles of interaction has been that the human will be explicit, unambiguous, and fully attentive while controlling the information and command flow. Boredom, preoccupation, and stress are unthinkable even though they are “very human” behaviors. This insensitivity of current HCI designs is fine for well-codified tasks. It works for making plane reservations, buying and selling stocks, and, as a matter of fact, almost everything we do with computers today. But this kind of categorical computing is inappropriate for design, debate, and deliberation. In fact, it is the major impediment to having flexible machines capable of adapting to their users and their level of attention, preferences, moods, and intentions. The ability to detect and understand affective states of a person we are communicating with is the core of emotional intelligence. Emotional intelligence (EQ) is a facet of human intelligence that has been argued to be indispensable and even the most important for a successful social life (Goleman, 1995). When it comes to computers, however, not all of them will need emotional intelligence and none will need all of the related skills that we need. Yet human–machine interactive systems capable of sensing stress, inattention, and heedfulness, and capable of adapting and responding appropriately to these affective states of the user are likely to be perceived as more natural, more efficacious, and more trustworthy. The research area of machine analysis of human affective states and employment of this information to build more natural, flexible (affective) HCI goes by a general name of affective computing, introduced first by Picard (1997).
Argument Structure Models and Visualization
Authors: Nissan Ephraim
Published: May 2011

In order to visualize argumentation, there exist tools from multimedia. The most advanced sides of computational modeling of arguments belong in models and tools upstream of visualization tools: the latter are an interface. Computer models of argumentation come in three categories: logic-based (highly theoretical), probablistic, and pragmatic ad hoc treatments. Theoretical formalisms of argumentation were developed by logicists within artificial intelligence (and were implemented and often can be reused outside the original applications), or then the formalisms are rooted in philosophers’ work. We cite some such work, but focus on tools that support argumentation visually. Argumentation turns out in a wide spectrum of everyday life situations, including professional ones. Computational models of argumentation have found application in tutoring systems, tools for marshalling legal evidence, and models of multiagent communication. Intelligent systems and other computer tools potentially stand to benefit as well. Multimedia are applied to argumentation (in visualization tools), and also are a promising field of application (in tutoring systems). The design of networks could potentially benefit, if communication is modeled using multiagent technology.
Blogs as Corporate Tools
Authors: Cortini Michela
Published: May 2011

According to The Weblog Handbook (Blood, 2003), Weblogs, or blogs as they are usually called, are online and interactive diaries, very similar to both link lists and online magazines. Up to now, the psychosocial literature on new technologies has studied primarly personal blogs, without giving too much interest to corporate blogs. This article aims to fill such a gap, examining blogs as corporate tools. Blogs are online diaries, where the blogger expresses himself herself, in an autoreferential format (Blood, 2003; Cortini, 2005), as the blogger would consider that only he or she deserves such attention. The writing is updated more than once a day, as the blogger needs to be constantly online and in constant contact with her audience. Besides diaries, there are also notebooks, which are generally more reflexive in nature. There are long comments on what is reported, and there is equilibrium in the discourse between the self and the rest of the world out there, in the shape of external links, as was seen in the first American blogs, which featured an intense debate over the Iraq war (Jensen, 2003). Finally, there are filters, which focus on external links. A blogger of a filter talks about himself or herself by talking about someone and something else and expresses himself or herself in an indirect way (Blood, 2003). In addition, filters, which are less esthetic and more frequently updated than diary blogs or Web sites since they have a practical aim, are generally organized around a thematic focus, which represents the core of the virtual community by which the filter lives.
Blogs in Education
Authors: Wang Shuyan
Published: May 2011

Blogs have been sprouting like mushrooms after rain in the past few years because of their effectiveness in keeping contact with friends, family, and anybody else who shares interests. Blogs have been used in every field in present society and are becoming the mainstream medium in communication and virtual communities. Politicians and political candidates use blogs to express opinions on political issues. Since the last presidential election, blogs have played a major role in helping candidates conduct outreach and opinion forming. Many famous journalists write their own blogs. Many film stars or personages create their blogs to communicate with their fans or followers. Even soldiers serving in the Iraq war keep blogs to show readers new perspective on the realities of war. Blogs are increasingly being used in education by researchers, teachers, and students. Most high school students or college students belong to one form a virtual community as they share interests and daily news. Scholars have started blogging in order to reflect on their research. More and more teachers are keeping research blogs or creating course blogs. Students are keeping course blogs or personal blogs.