Task-based image annotation and retrieval

In order to address problems of information overload in digital imagery task domains we have developed an interactive approach to the capture and reuse of image context information. Our framework models different aspects of the relationship between images and domain tasks they support by monitoring the interactive manipulation and annotation of task-relevant imagery. The approach allows us to gauge a measure of a user's intentions as they complete goal-directed image tasks. As users analyze retrieved imagery their interactions are captured and an expert task context is dynamically constructed. This human expertise, proficiency, and knowledge can then be leveraged to support other users in carrying out similar domain tasks. We have applied our techniques to two multimedia retrieval applications for two different image domains, namely the geo-spatial and medical imagery domains. © Springer-Verlag Berlin Heidelberg 2007.

Knowledge management for more sustainable water systems

The management and sharing of complex data, information and knowledge is a fundamental and growing concern in the Water and other Industries for a variety of reasons. For example, risks and uncertainties associated with climate, and other changes require knowledge to prepare for a range of future scenarios and potential extreme events. Formal ways in which knowledge can be established and managed can help deliver efficiencies on acquisition, structuring and filtering to provide only the essential aspects of the knowledge really needed. Ontologies are a key technology for this knowledge management. The construction of ontologies is a considerable overhead on any knowledge management programme. Hence current computer science research is investigating generating ontologies automatically from documents using text mining and natural language techniques. As an example of this, results from application of the Text2Onto tool to stakeholder documents for a project on sustainable water cycle management in new developments are presented. It is concluded that by adopting ontological representations sooner, rather than later in an analytical process, decision makers will be able to make better use of highly knowledgeable systems containing automated services to ensure that sustainability considerations are included. © 2010 The authors.

Upgradable Tree Levels Editor of Metaontologies, Ontologies and Knowledge for a Chemistry

* This paper has supported by Far Eastern Branch of the Russian Academy of Sciences, the project 06-III-A-01-005 and Russian Fund of Fundamental Investigation, the project 06-07-89071-a

Classification of Data to Extract Knowledge from Neural Networks

A major drawback of artificial neural networks is their black-box character. Therefore, the rule extraction algorithm is becoming more and more important in explaining the extracted rules from the neural networks. In this paper, we use a method that can be used for symbolic knowledge extraction from neural networks, once they have been trained with desired function. The basis of this method is the weights of the neural network trained. This method allows knowledge extraction from neural networks with continuous inputs and output as well as rule extraction. An example of the application is showed. This example is based on the extraction of average load demand of a power plant.

Classification of Smoking Cessation Status Using Various Data Mining Methods

AMS Subj. Classification: 62P10, 62H30, 68T01

Digital Repository of Information and Knowledge - Fund “BellKnow”

The aim was to develop an archive containing detailed description of church bells. As an object of cultural heritage the bell has general properties such as geometric dimensions, weight, sound of each of the bells, the pitch of the tone as well as acoustical diagrams obtained using contemporary equipment. The audio, photo and video archive is developed by using advanced technologies for analysis, reservation and data protection.

Applying Data Mining Tools in E-Learning

Report published in the Proceedings of the National Conference on "Education and Research in the Information Society", Plovdiv, May, 2014

Data Mining for Software Development Life Cycle Quality Management

Computer software plays an important role in business, government, society and sciences. To solve real-world problems, it is very important to measure the quality and reliability in the software development life cycle (SDLC). Software Engineering (SE) is the computing field concerned with designing, developing, implementing, maintaining and modifying software. The present paper gives an overview of the Data Mining (DM) techniques that can be applied to various types of SE data in order to solve the challenges posed by SE tasks such as programming, bug detection, debugging and maintenance. A specific DM software is discussed, namely one of the analytical tools for analyzing data and summarizing the relationships that have been identified. The paper concludes that the proposed techniques of DM within the domain of SE could be well applied in fields such as Customer Relationship Management (CRM), eCommerce and eGovernment. ACM Computing Classification System (1998): H.2.8.

Investigation of factors influencing loyalty – The role of involvement, perceived risk and knowledge

Our research aimed to reveal the effects that can be observed during the buying process of food products and can influence the decisions of customers. We focused on the role of enduring involvement in customers’ behavioural loyalty, that is, the repurchase of food brands. To understand this relationship in a more sophisticated way, we involved two mediating constructs in our conceptual model: perceived risk and perceived knowledge of food products. The data collection was carried out among undergraduate students in frame of an online survey, and we used SPSS/AMOS software to test the model. The results only partly supported our hypothesis, although the involvement effects on loyalty and the two mediating constructs were strong enough, loyalty couldn’t be explained well by perceived risk and knowledge. The roles of further mediating/moderating variables should be determined and investigated in the next section of the research series.

The species composition of true bug assemblages extracted with Berlese funnels (Data to the knowledge on the ground-living Heteroptera of Hungary, No 1)

A rich collection of Heteroptera extracted with Berlese funnel by Dr. I. Loksa between 1953–1974 in Hungary, has been examined. Altogether 157 true bug species have been identified. The great majority of them have been found in very low number, there are only 27 species of which more than 10 adult individuals have been found. Some species considered to be rare or very rare in Hungary have been collected in relatively great number (Ceratocombus coleoptratus, Cryptostemma pusillimum, C. waltli, Acalypta carinata, A. platycheila, Loricula ruficeps, Myrmedobia exilis). The three families, which are more or less rich in species and have the highest ratio of extracted species, were Rhyparochromidae, Tingidae and Nabidae. Out of them, the family Rhyparochromidae has been found to be most diverse and most characteristic at the ground-level. Individuals of the families Tingidae, Hebridae and Rhyparochromidae have been found in greatest number. The occurrence of the lace bug Campylosteira orientalis Horváth, 1881 in Hungary has been verified by a voucher specimen. In respect to the environmental changes through the country, parallel changes have been observed in the zoogeographical distribution of the ground-living bugs.

Ecology of the Acalypta species occurring in Hungary Insecta Heteroptera Tingidae data to the knowledge on the ground-living Heteroptera of Hungary, No 3

As a third part of a series of papers on the ground-living true bugs of Hungary, the species belonging to the lace bug genus Acalypta Westwood, 1840 (Insecta: Heteroptera: Tingidae) were studied. Extensive materials collected with Berlese funnels during about 20 years all over Hungary were identified. Based on these sporadic data of many years, faunistic notes are given on some Hungarian species. The seasonal occurrence of the species are discussed. The numbers of specimens of different Acalypta species collected in diverse plant communities are compared with multivariate methods. Materials collected with pitfall traps between 1979–1982 at Bugac, Kiskunság National Park were also processed. In this area, only A. marginata and A. gracilis occurred, both in great number. The temporal changes of the populations are discussed. Significant differences could be observed between the microhabitat distribution of the two species: both species occurred in very low number in traps placed out in patches colonized by dune-slack purple moorgrass meadow; Acalypta gracilis preferred distinctly the Pannonic dune open grassland patches; A. marginata occurred in almost equal number in Pannonic dune open grassland and in Pannonic sand puszta patches.

Development of prediction models for freeway incident durations using data mining techniques

The nation's freeway systems are becoming increasingly congested. A major contribution to traffic congestion on freeways is due to traffic incidents. Traffic incidents are non-recurring events such as accidents or stranded vehicles that cause a temporary roadway capacity reduction, and they can account for as much as 60 percent of all traffic congestion on freeways. One major freeway incident management strategy involves diverting traffic to avoid incident locations by relaying timely information through Intelligent Transportation Systems (ITS) devices such as dynamic message signs or real-time traveler information systems. The decision to divert traffic depends foremost on the expected duration of an incident, which is difficult to predict. In addition, the duration of an incident is affected by many contributing factors. Determining and understanding these factors can help the process of identifying and developing better strategies to reduce incident durations and alleviate traffic congestion. A number of research studies have attempted to develop models to predict incident durations, yet with limited success. ^ This dissertation research attempts to improve on this previous effort by applying data mining techniques to a comprehensive incident database maintained by the District 4 ITS Office of the Florida Department of Transportation (FDOT). Two categories of incident duration prediction models were developed: "offline" models designed for use in the performance evaluation of incident management programs, and "online" models for real-time prediction of incident duration to aid in the decision making of traffic diversion in the event of an ongoing incident. Multiple data mining analysis techniques were applied and evaluated in the research. The multiple linear regression analysis and decision tree based method were applied to develop the offline models, and the rule-based method and a tree algorithm called M5P were used to develop the online models. ^ The results show that the models in general can achieve high prediction accuracy within acceptable time intervals of the actual durations. The research also identifies some new contributing factors that have not been examined in past studies. As part of the research effort, software code was developed to implement the models in the existing software system of District 4 FDOT for actual applications. ^

A multi-method approach for the assessment of composite indices and rankings

There is growing popularity in the use of composite indices and rankings for cross-organizational benchmarking. However, little attention has been paid to alternative methods and procedures for the computation of these indices and how the use of such methods may impact the resulting indices and rankings. This dissertation developed an approach for assessing composite indices and rankings based on the integration of a number of methods for aggregation, data transformation and attribute weighting involved in their computation. The integrated model developed is based on the simulation of composite indices using methods and procedures proposed in the area of multi-criteria decision making (MCDM) and knowledge discovery in databases (KDD). The approach developed in this dissertation was automated through an IT artifact that was designed, developed and evaluated based on the framework and guidelines of the design science paradigm of information systems research. This artifact dynamically generates multiple versions of indices and rankings by considering different methodological scenarios according to user specified parameters. The computerized implementation was done in Visual Basic for Excel 2007. Using different performance measures, the artifact produces a number of excel outputs for the comparison and assessment of the indices and rankings. In order to evaluate the efficacy of the artifact and its underlying approach, a full empirical analysis was conducted using the World Bank's Doing Business database for the year 2010, which includes ten sub-indices (each corresponding to different areas of the business environment and regulation) for 183 countries. The output results, which were obtained using 115 methodological scenarios for the assessment of this index and its ten sub-indices, indicated that the variability of the component indicators considered in each case influenced the sensitivity of the rankings to the methodological choices. Overall, the results of our multi-method assessment were consistent with the World Bank rankings except in cases where the indices involved cost indicators measured in per capita income which yielded more sensitive results. Low income level countries exhibited more sensitivity in their rankings and less agreement between the benchmark rankings and our multi-method based rankings than higher income country groups.

Hospitality Data Mining Myths

Electronic database handling of buisness information has gradually gained its popularity in the hospitality industry. This article provides an overview on the fundamental concepts of a hotel database and investigates the feasibility of incorporating computer-assisted data mining techniques into hospitality database applications. The author also exposes some potential myths associated with data mining in hospitaltiy database applications.