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"2016-02-11T05:56:44Z" } }, { "key": "HWHV6U7I", "version": 15, "library": { "type": "group", "id": 477211, "name": "PROV-504", "links": { "alternate": { "href": "https://www.zotero.org/groups/prov-504", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/477211/items/HWHV6U7I", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/prov-504/items/HWHV6U7I", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2912815, "username": "nkacham", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/nkacham", "type": "text/html" } } }, "creatorSummary": "Kwon et al.", "parsedDate": "2014-06", "numChildren": 1 }, "data": { "key": "HWHV6U7I", "version": 15, "itemType": "journalArticle", "title": "Data quality management, data usage experience and acquisition intention of big data analytics", "creators": [ { "creatorType": "author", "firstName": "Ohbyung", "lastName": "Kwon" }, { "creatorType": "author", "firstName": "Namyeon", "lastName": "Lee" }, { "creatorType": "author", "firstName": "Bongsik", "lastName": "Shin" } ], "abstractNote": "Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics mainly from the theoretical perspectives of data quality management and data usage experience. Our empirical investigation reveals that a firm's intention for big data analytics can be positively affected by its competence in maintaining the quality of corporate data. Moreover, a firm's favorable experience (i.e., benefit perceptions) in utilizing external source data could encourage future acquisition of big data analytics. Surprisingly, a firm's favorable experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics.", "publicationTitle": "International Journal of Information Management", "publisher": "", "place": "", "date": "June 2014", "volume": "34", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "387-394", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "International Journal of Information Management", "DOI": "10.1016/j.ijinfomgt.2014.02.002", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S0268401214000127", "accessDate": "2016-02-11T05:51:40Z", "PMID": "", "PMCID": "", "ISSN": "0268-4012", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Big data analytics", "type": 1 }, { "tag": "Data quality management", "type": 1 }, { "tag": "Data usage", "type": 1 }, { "tag": "IT capability", "type": 1 }, { "tag": "Resource-based view", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-02-11T05:56:44Z", "dateModified": "2016-02-11T05:56:44Z" } }, { "key": "GTSBJKPU", "version": 15, "library": { "type": "group", "id": 477211, "name": "PROV-504", "links": { "alternate": { "href": "https://www.zotero.org/groups/prov-504", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/477211/items/GTSBJKPU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/prov-504/items/GTSBJKPU", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2912815, "username": "nkacham", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/nkacham", "type": "text/html" } } }, "creatorSummary": "Diamantoulakis et al.", "parsedDate": "2015-09", "numChildren": 1 }, "data": { "key": "GTSBJKPU", "version": 15, "itemType": "journalArticle", "title": "Big Data Analytics for Dynamic Energy Management in Smart Grids", "creators": [ { "creatorType": "author", "firstName": "Panagiotis D.", "lastName": "Diamantoulakis" }, { "creatorType": "author", "firstName": "Vasileios M.", "lastName": "Kapinas" }, { "creatorType": "author", "firstName": "George K.", "lastName": "Karagiannidis" } ], "abstractNote": "The smart electricity grid enables a two-way flow of power and data between suppliers and consumers in order to facilitate the power flow optimization in terms of economic efficiency, reliability and sustainability. This infrastructure permits the consumers and the micro-energy producers to take a more active role in the electricity market and the dynamic energy management (DEM). The most important challenge in a smart grid (SG) is how to take advantage of the users' participation in order to reduce the cost of power. However, effective DEM depends critically on load and renewable production forecasting. This calls for intelligent methods and solutions for the real-time exploitation of large volumes of data generated by the vast amount of smart meters. Hence, robust data analytics, high performance computing, efficient data network management, and cloud computing techniques are critical towards the optimized operation of SGs. This research aims to highlight the big data issues and challenges faced by the DEM employed in SG networks. It also provides a brief description of the most commonly used data processing methods in the literature, and proposes a promising direction for future research in the field.", "publicationTitle": "Big Data Research", "publisher": "", "place": "", "date": "September 2015", "volume": "2", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "94-101", "series": "Big Data, Analytics, and High-Performance Computing", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Big Data Research", "DOI": "10.1016/j.bdr.2015.03.003", "citationKey": "", "url": "http://www.sciencedirect.com/science/article/pii/S2214579615000283", "accessDate": "2016-02-11T05:53:31Z", "PMID": "", "PMCID": "", "ISSN": "2214-5796", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ScienceDirect", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Artificial intelligence", "type": 1 }, { "tag": "Big data", "type": 1 }, { "tag": "Dynamic energy management", "type": 1 }, { "tag": "High performance computing", "type": 1 }, { "tag": "Predictive analytics", "type": 1 }, { "tag": "Smart grids", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-02-11T05:56:44Z", "dateModified": "2016-02-11T05:56:44Z" } }, { "key": "WGD9E57Z", "version": 15, "library": { "type": "group", "id": 477211, "name": "PROV-504", "links": { "alternate": { "href": "https://www.zotero.org/groups/prov-504", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/477211/items/WGD9E57Z", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/prov-504/items/WGD9E57Z", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2912815, "username": "nkacham", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/nkacham", "type": "text/html" } } }, "creatorSummary": "van der Vlist", "parsedDate": "2016-02-09", "numChildren": 0 }, "data": { "key": "WGD9E57Z", "version": 15, "itemType": "journalArticle", "title": "Accounting for the social: Investigating commensuration and Big Data practices at Facebook", "creators": [ { "creatorType": "author", "firstName": "F. N.", "lastName": "van der Vlist" } ], "abstractNote": "", "publicationTitle": "Big Data & Society", "publisher": "", "place": "", "date": "2016-02-09", "volume": "3", "issue": "1", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1177/2053951716631365", "citationKey": "", "url": "http://bds.sagepub.com/lookup/doi/10.1177/2053951716631365", "accessDate": "2016-02-11T05:52:46Z", "PMID": "", "PMCID": "", "ISSN": "2053-9517", "archive": "", "archiveLocation": "", "shortTitle": "Accounting for the social", "language": "en", "libraryCatalog": "CrossRef", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-02-11T05:56:44Z", "dateModified": "2016-02-11T05:56:44Z" } }, { "key": "WTQX28A6", "version": 13, "library": { "type": "group", "id": 477211, "name": "PROV-504", "links": { "alternate": { "href": "https://www.zotero.org/groups/prov-504", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/477211/items/WTQX28A6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/prov-504/items/WTQX28A6", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2912815, "username": "nkacham", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/nkacham", "type": "text/html" } } }, "creatorSummary": "Dean and Ghemawat", "parsedDate": "2008-01", "numChildren": 0 }, "data": { "key": "WTQX28A6", "version": 13, "itemType": "journalArticle", "title": "MapReduce: Simplified Data Processing on Large Clusters", "creators": [ { "creatorType": "author", "firstName": "Jeffrey", "lastName": "Dean" }, { "creatorType": "author", "firstName": "Sanjay", "lastName": "Ghemawat" } ], "abstractNote": "MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.", "publicationTitle": "Commun. 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As educational researchers become increasingly interested in the relationship between form of representation and form of understanding, new representational forms are being used to cońvey to \"readers\" what has been learned. These explorations are rooted in an expanding conception of the nature of knowledge and the relationship between what one knows and how it is represented. While new forms of representation have their potential virtues, they also have their limitations. 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Methods for discrete population genetic data.", "creators": [ { "creatorType": "author", "firstName": "B. S.", "lastName": "Weir" } ], "abstractNote": "This book describes, in detail, statistical methods used in the analysis of population genetic data of a discrete (enumeration) nature, such as genotype frequencies. It is not concerned with the analysis of continuously variable traits. It is the author's hope that the book will bridge the gap between Elandt-Johnson's Probability Models and Statistical Methods in Genetics, published 20 years ago, and the range of techniques currently encountered in the scientific literature. The introductory chapter deals with the nature of genetic data and sampling, notation and terminology, and discusses the historical controversy over the goodness of fit of Mendel's experimental data to theoretical expectation. There follow chapters on the following: estimating frequencies (multinomial genotypic counts, likelihood estimation, etc); disequilibrium (Hardy-Weinberg, linkage); measures of gene diversity and heterozygosity; population structure (including genetic distance); analyses between generations (including estimation of outcrossing, selection and linkage, and inferences about paternity); molecular data (restriction fragment analysis, DNA sequence comparisons, etc); phylogeny construction. There are appendices on statistical distributions, random numbers, and FORTRAN source listings for programs on linkage disequilibrium, F-statistics (Wright) and DNA sequence manipulation. Each chapter has a summary, and exercises for the reader to carry out.", "publicationTitle": "", "publisher": "", "place": "", "date": "1990", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "xii + 377pp.", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "CABDirect2", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "CABI - CAB Abstracts", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2016-02-10T07:59:05Z", "dateModified": "2016-02-10T07:59:05Z" } }, { "key": "QA3KKCU2", "version": 6, "library": { "type": "group", "id": 477211, "name": "PROV-504", "links": { "alternate": { "href": "https://www.zotero.org/groups/prov-504", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/477211/items/QA3KKCU2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/prov-504/items/QA3KKCU2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2912815, "username": "nkacham", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/nkacham", "type": "text/html" } } }, "creatorSummary": "Tsytsarau and Palpanas", "parsedDate": "2012-05", "numChildren": 0 }, "data": { "key": "QA3KKCU2", "version": 6, "itemType": "journalArticle", "title": "Survey on Mining Subjective Data on the Web", "creators": [ { "creatorType": "author", "firstName": "Mikalai", "lastName": "Tsytsarau" }, { "creatorType": "author", "firstName": "Themis", "lastName": "Palpanas" } ], "abstractNote": "In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers' attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.", "publicationTitle": "Data Min. Knowl. Discov.", "publisher": "", "place": "", "date": "May 2012", "volume": "24", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "478–514", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1007/s10618-011-0238-6", "citationKey": "", "url": "http://dx.doi.org/10.1007/s10618-011-0238-6", "accessDate": "2016-02-10T07:25:20Z", "PMID": "", "PMCID": "", "ISSN": "1384-5810", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ACM Digital Library", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Contradiction analysis", "type": 1 }, { "tag": "Opinion mining", "type": 1 }, { "tag": "Sentiment analysis", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2016-02-10T07:35:41Z", "dateModified": "2016-02-10T07:35:41Z" } } ]