The computing devices with which we interact daily continue to become ever smaller, intelligent, and pervasive. Not only are they becoming more intelligent, but some are developing awareness of a user's affective state. Affective computing—computing that in some way senses, expresses, or modifies affect—is still a field very much in its youth. While progress has been made, the field is still limited by the need for larger sets of diverse, naturalistic, and multimodal data. This work first considers effective strategies for designing psychophysiological studies that permit the assembly of very large samples that cross numerous demographic boundaries, data collection in naturalistic environments, distributed study locations, rapid iterations on study designs, and the simultaneous investigation of multiple research questions. It then explores how commodity hardware and general-purpose software tools can be used to record, represent, store, and disseminate such data. As a realization of these strategies, this work presents a new database from the Emotion in Motion (EiM) study of human psychophysiological response to musical affective stimuli comprising over 23,000 participants and nearly 67,000 psychophysiological responses. Because music presents an excellent tool for the investigation of human response to affective stimuli, this work uses this wealth of data to explore how to design more effective affective computing systems by characterizing the strongest responses to musical stimuli used in EiM. This work identifies and characterizes the strongest of these responses, with a focus on modeling the characteristics of listeners that make them more or less prone to demonstrating strong physiological responses to music stimuli. This dissertation contributes the findings from a number of explorations of the relationships between strong reactions to music and the characteristics and self-reported affect of listeners. It demonstrates not only that such relationships do exist, but takes steps toward automatically predicting whether or not a listener will exhibit such exceptional responses. Second, this work contributes a flexible strategy and functional system for both successfully executing large-scale, distributed studies of psychophysiology and affect; and for synthesizing, managing, and disseminating the data collected through such efforts. Finally, and most importantly, this work presents the EiM database itself. / Doctor of Philosophy / The computing devices with which we interact daily continue to become ever smaller, intelligent, and pervasive. Not only are they becoming more intelligent, but some are developing awareness of a user’s affective state. Affective computing—computing that in some way senses, expresses, or modifies affect—is still a field very much in its youth. While progress has been made, the field is still limited by the need for larger sets of diverse, naturalistic, and multimodal data. This dissertation contributes the findings from a number of explorations of the relationships between strong reactions to music and the characteristics and self-reported affect of listeners. It demonstrates not only that such relationships do exist, but takes steps toward automatically predicting whether or not a listener will exhibit such exceptional responses. Second, this work contributes a flexible strategy and functional system for both successfully executing large-scale, distributed studies of psychophysiology and affect; and for synthesizing, managing, and disseminating the data collected through such efforts. Finally, and most importantly, this work presents the Emotion in Motion (EiM) (a study of human affective/psychophysiological response to musical stimuli) database comprising over 23,000 participants and nearly 67,000 psychophysiological responses.

Online Multimedia Management System (OMMS) is a project to provide users to store and share their mutimedia files. The users and administrator can modify and update the multimedia files and database information from a normal web browser. The administrator and users with access have different levels of permission.

Thesis (doctoral)--Humboldt Universität, Berlin, 2003. / Description based on print version record. Includes bibliographical references (p. [107]-113) and index. Also issued online.

Non-relational database management systems address some of the limitations relational database management systems have when storing large volumes of unstructured, user generated text-based data in distributed environments. They follow different approaches through the data model they use, their ability to scale data storage over distributed servers and the programming interface they provide. An experimental approach was followed to measure the capabilities these alternative database management systems present in their approach to address the limitations of relational databases in terms of their capability to store unstructured text-based data, data warehousing capabilities, ability to scale data storage across distributed servers and the level of programming abstraction they provide. The results of the research highlighted the limitations of relational database management systems. The different database management systems do address certain limitations, but not all. Document-oriented databases provide the best results and successfully address the need to store large volumes of user generated text-based data in a distributed environment / School of Computing / M. Sc. (Computer Science)

The project developed eBay Learning Center System (ELCS), a web-based application that provides current and potential eBay users a way to learn about the many functions of the popular online auction and shopping web site and be successful eBay traders. ELCS provides end users with online tutorials, available both in multimedia and text formats, and methods of communicating with system administrators and other users by means of a message box and a discussion forum to facilitate learning and collaborative problem solving. The system employs current technologies such as SQL, HTML, ASP.NET, VBScript, XML, ODBC, and ADO.

There are many types of databases, but the most common are relational and non-relational. These databases have different structures, and that affects their performance. Many studies examine the differences between relational and non-relational databases and compare them regarding performance. However, it lacks a study that collects the results from different sources and makes them available to software professionals, so they can choose a suitable database effortlessly. This thesis examines and analyzes several studies investigating the performance of relational and non-relational databases. The analysis examines the performance of typical database operations, insert, delete, update, and select, on different numbers of records. The results of this study show that the non-relational databases perform better, regardless of the number of records in the database. However, there are some cases where the relational databases perform better. The findings are based on an analysis of seven studies, encompassing databases MSSQL, MySQL, PostgreSQL, Oracle, and MongoDB. / Det finns flera typer av databaser, men de vanligaste är relationella och icke-relationella. Dessa databaser har olika strukturer, vilket påverkar deras prestanda. Många studier undersöker skillnaderna mellan relationella och icke-relationella databaser och jämför deras prestanda. Dock saknas en studie som samlar resultaten från olika källor och gör dem tillgängliga för mjukvaruproffs, för att underlätta valet av en lämplig databas. Denna examensarbete undersöker och analyserar flera studier som utforskar prestandan hos relationella och icke-relationella databaser. Analysen fokuserar på prestandan för vanliga databasoperationer, såsom infogning, borttagning, uppdatering och val, för olika antal poster. Resultaten av denna studie visar att icke-relationella databaser presterar bättre oavsett antalet poster i databasen. Det finns dock vissa fall där relationella databaser fungerar bättre. Resultaten baseras på en analys av sju studier som omfattar MSSQL, MySQL, PostgreSQL, Oracle och MongoDB-databaserna.

Indiana University-Purdue University Indianapolis (IUPUI) / Cytokines mediate crucial functions in innate and adaptive immunity. They play valuable roles in immune cell growth and lineage specification, and are associated with various disease pathologies. A large number of low, medium and high throughput studies have implicated association of single nucleotide polymorphisms (SNPs) in cytokine genes with diseases. A preponderance of such experiments have not shown any causality of an identified SNP to the associated disease. Instead, they have identified statistically significant SNP-disease associations; hence, it is likely that some of these cytokine gene variants may directly or indirectly cause the disease phenotype(s). To fill this knowledge gap and derive study parameters for cytokine SNP-disease causality relationships, we have designed and developed the Disease Associated Cytokine SNP Database (DACS-DB). DACS-DB has data on 456 cytokine genes, approximately 61,000 SNPs, and 891 SNP-associated diseases. In DACS-DB, among other attributes, we present functional annotation, and heterozygosity allele frequency for the SNPs, and literature-validated SNP association for diseases. Users of the DB can run queries such as the ones to find disease-associated SNPs in a cytokine gene, and all the SNPs involved in a disease. We have developed a web front end (available at http://www.iupui.edu/~cytosnp) to disseminate this information for immunologists, biomedical researchers, and other interested biological researchers. Since there is no such comprehensive collection of disease associated cytokine SNPs, this DB will be vital to understanding the role of cytokine SNPs as markers in disease, and, more importantly, in causality to disease thus helping to identify drug targets for common inflammatory diseases. Due to the presence of rich annotations, the DACS-DB can be a good source for building a tool for the prediction of the "disease association potential (DAP)" of a given SNP. In a preliminary effort to devise such a methodology for DAP prediction, we have applied a support vector machine (SVM) to classify SNPs. Employing the SNP attributes of function class, heterozygosity value, and heterozygosity standard error, 864 SNPs were classified into two classes, "disease" and "non-disease". The SVM returned a classification of these SNPs into the disease and non-disease classes with an accuracy of 74%. By modifying various SNP and disease attributes in the training data sets, such a predictive algorithm can be extrapolated to identify potential disease associated SNPs among newly sequenced cytokine variations. In the long run, this approach can provide a means for future gene variation based therapeutic regimens.

No description available.

Thesis (MComm)--Stellenbosch University, 2013. / ENGLISH ABSTRACT: NoSQL databases, a new way of storing and retrieving data, can provide businesses with many benefits, although they also pose many risks for businesses. The lack of knowledge among decision-makers of businesses regarding NoSQL databases can lead to risks left unaddressed and missed opportunities. This study, by means of an extensive literature review, identifies the key drivers, characteristics and benefits of a NoSQL database, thereby providing a clear understanding of the subject. The business imperatives related to NoSQL databases are also identified and discussed. This can help businesses to determine whether a NoSQL database might be a viable solution, and to align business and information technology (IT) objectives. The key strategic and operational IT risks are also identified and discussed, based on the literature review. This can help business to ensure that the risks related to the use of NoSQL databases are appropriately addressed. Lastly, the identified risks were mapped to the processes of COBIT (Control Objectives for Information and Related Technology) to inform a business of the highest risk areas and the associated focus areas. / AFRIKAANSE OPSOMMING: NoSQL databasisse, 'n nuwe manier om data te stoor en herwin, het die potensiaal om baie voordele vir besighede in te hou, maar kan ook baie risiko's teweeg bring. Gebrekkige kennis onder besigheidsbesluitnemers oor NoSQL databasisse kan lei tot onaangespreekte risiko’s en verlore geleenthede. Hierdie studie, deur middel van 'n uitgebreide literatuuroorsig, identifiseer die sleutel eienskappe, kenmerke en voordele van 'n NoSQL databasis, om sodoende 'n duidelike begrip van die onderwerp te verkry. Die besigheidsimperatiewe wat verband hou met NoSQL databasisse is ook geïdentifiseer en bespreek. Dit kan besighede help om te bepaal of 'n NoSQL databasis 'n werkbare oplossing kan wees, asook sake- en inligtingstegnologie (IT) doelwitte in lyn met mekaar bring. Na aanleiding van die literatuurstudie is die sleutel-strategiese en operasionele IT-risiko's geïdentifiseer en bespreek. Dit kan help om aan besighede sekerheid te verskaf dat die risiko's wat verband hou met die gebruik van NoSQL databasisse toepaslik aangespreek word. Laastens is die geïdentifiseerde risiko's gekoppel aan die prosesse van COBIT om 'n besigheid van die hoë-risiko areas en die gepaardgaande fokusareas in te lig.

published_or_final_version / Computer Science and Information Systems / Master / Master of Philosophy