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}, "data": { "key": "SS6HTQQX", "version": 1, "itemType": "document", "title": "Agent-based Visualization of Streaming Text", "creators": [ { "creatorType": "author", "firstName": "Jordan", "lastName": "Riley Benson" }, { "creatorType": "author", "firstName": "Phillip", "lastName": "Lafleur" }, { "creatorType": "author", "firstName": "David", "lastName": "Crist" }, { "creatorType": "author", "firstName": "Benjamin", "lastName": "Watson" } ], "abstractNote": "We present a visualization infrastructure that maps data elements\nto agents, which have behaviors parameterized by those elements.\nDynamic visualizations emerge as the agents change position,\nalter appearance and respond to one other. Agents move to\nminimize the difference between displayed agent-to-agent\ndistances, and an input matrix of ideal distances. Our current\napplication is visualization of streaming text. Each agent\nrepresents a significant word, visualizing it by displaying the word\nitself, centered in a circle sized by the frequency of word\noccurrence. We derive the ideal distance matrix from word cooccurrence,\nmapping higher co-occurrence to lower distance. To\ndepict co-occurrence in its textual context, the ratio of intersection\nto circle area approximates the ratio of word co-occurrence to\nfrequency. A networked backend process gathers articles from\nnews feeds, blogs, Digg or Twitter, exploiting online search APIs\nto focus on user-chosen topics. Resulting visuals reveal the\nprimary topics in text streams as clusters, with agent-based layout\nmoving without instability as data streams change dynamically.", "type": "", "date": "", "publisher": "", "place": "", "DOI": "", "citationKey": "", "url": "http://designgraphics.ncsu.edu/pages/docs/infovis08.skimmer.pdf", "accessDate": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-05-16T19:30:50Z", "dateModified": "2010-05-16T19:30:50Z" } }, { "key": "8NXHK2ZQ", "version": 1, "library": { "type": "group", "id": 17242, "name": "Stream Mining", "links": { "alternate": { "href": "https://www.zotero.org/groups/stream_mining", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/17242/items/8NXHK2ZQ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/stream_mining/items/8NXHK2ZQ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 235284, "username": "buki", "name": "Andreas Bauer", "links": { "alternate": { "href": "https://www.zotero.org/buki", "type": "text/html" } } }, "creatorSummary": "Sayyadi et al.", "numChildren": 0 }, "data": { "key": "8NXHK2ZQ", "version": 1, "itemType": "document", "title": "Event Detection and Tracking in Social Streams", "creators": [ { "creatorType": "author", "firstName": "Hassan", "lastName": "Sayyadi" }, { "creatorType": "author", "firstName": "Matthew", "lastName": "Hurst" }, { "creatorType": "author", "firstName": "Alexey", "lastName": "Maykov" } ], "abstractNote": "Events and stories can be characterized by a set of descriptive,\ncollocated keywords. 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Constellations of keywords\ndescribing an event may be used to find related articles.\nWe also use the proposed algorithm to analyze events\nand track stories in social streams.", "type": "", "date": "", "publisher": "", "place": "", "DOI": "", "citationKey": "", "url": "http://www.cs.umd.edu/~sayyadi/files/papers/9-sayyadi-EventDetection_KeyGraph_ICWSM09.pdf", "accessDate": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-05-16T10:19:36Z", "dateModified": "2010-05-16T10:20:51Z" } }, { "key": "HZPAQVZX", "version": 1, "library": { "type": "group", "id": 17242, "name": "Stream Mining", "links": { "alternate": { "href": "https://www.zotero.org/groups/stream_mining", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/17242/items/HZPAQVZX", "type": "application/json" }, "alternate": { "href": 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Adam", "lastName": "Covington" }, { "creatorType": "author", "firstName": "Ronald P", "lastName": "Loui" }, { "creatorType": "author", "firstName": "John W", "lastName": "Lockwood" }, { "creatorType": "author", "firstName": "Young H", "lastName": "Cho" } ], "abstractNote": "", "publicationTitle": "", "publisher": "", "place": "", "date": "", "volume": "", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "", "url": "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.129.1590", "accessDate": "2010-05-04T20:59:28Z", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-05-15T13:04:55Z", "dateModified": "2010-05-15T13:04:55Z" } }, { "key": "GTIMZUBU", "version": 1, "library": { "type": "group", "id": 17242, "name": "Stream Mining", "links": { "alternate": { "href": "https://www.zotero.org/groups/stream_mining", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/17242/items/GTIMZUBU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/stream_mining/items/GTIMZUBU", "type": "text/html" } }, "meta": { "createdByUser": { "id": 235284, "username": "buki", "name": "Andreas Bauer", "links": { "alternate": { "href": "https://www.zotero.org/buki", "type": "text/html" } } }, "numChildren": 1 }, "data": { "key": "GTIMZUBU", "version": 1, "itemType": "webpage", "title": "DBLP Record 'conf/sdm/AggarwalY06'", "creators": [], "abstractNote": "", "websiteTitle": "", "websiteType": "", "date": "", "publisher": "", "place": "", "DOI": "", "citationKey": "", "url": "http://dblp.uni-trier.de/rec/bibtex/conf/sdm/AggarwalY06", "accessDate": "2010-05-04T21:05:40Z", "shortTitle": "", "language": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-05-15T13:04:55Z", "dateModified": "2010-05-15T13:04:55Z" } }, { "key": "44IUZ3MG", "version": 1, "library": { "type": "group", "id": 17242, "name": "Stream Mining", "links": { "alternate": { "href": "https://www.zotero.org/groups/stream_mining", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/17242/items/44IUZ3MG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/stream_mining/items/44IUZ3MG", "type": "text/html" } }, "meta": { "createdByUser": { "id": 235284, "username": "buki", "name": "Andreas Bauer", "links": { "alternate": { "href": "https://www.zotero.org/buki", "type": "text/html" } } }, "creatorSummary": "Karnstedt et al.", "parsedDate": "2009", "numChildren": 2 }, "data": { "key": "44IUZ3MG", "version": 1, "itemType": "conferencePaper", "title": "Adaptive burst detection in a stream engine", "creators": [ { "creatorType": "author", "firstName": "Marcel", "lastName": "Karnstedt" }, { "creatorType": "author", "firstName": "Daniel", "lastName": "Klan" }, { "creatorType": "author", "firstName": "Christian", "lastName": "Pölitz" }, { "creatorType": "author", "firstName": "Kai-Uwe", "lastName": "Sattler" }, { "creatorType": "author", "firstName": "Conny", "lastName": "Franke" } ], "abstractNote": "Detecting bursts in data streams is an important and challenging task. Due to the complexity of this task, usually burst detection cannot be formulated using standard query operators. Therefore, we show how to integrate burst detection for stationary as well as non-stationary data into query formulation and processing, from the language level to the operator level. Afterwards, we present fundamentals of threshold-based burst detection. We focus on the applicability of time series forecasting techniques in order to dynamically identify suitable thresholds for stream data containing arbitrary trends and periods. The proposed approach is evaluated with respect to quality and performance on synthetic and real-world sensor data using a full-fledged DSMS.", "proceedingsTitle": "Proceedings of the 2009 ACM symposium on Applied Computing", "conferenceName": "", "publisher": "ACM", "place": "Honolulu, Hawaii", "date": "2009", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "1511-1515", "series": "", "seriesNumber": "", "DOI": "10.1145/1529282.1529619", "ISBN": "978-1-60558-166-8", "citationKey": "", "url": "http://portal.acm.org/citation.cfm?id=1529619", "accessDate": "2010-05-04T21:04:31Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "ACM", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2010-05-15T13:04:55Z", "dateModified": "2010-05-15T13:04:55Z" } }, { "key": "ZIUQ7HGU", "version": 1, "library": { "type": "group", "id": 17242, "name": "Stream Mining", "links": { "alternate": { "href": "https://www.zotero.org/groups/stream_mining", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/17242/items/ZIUQ7HGU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/stream_mining/items/ZIUQ7HGU", "type": "text/html" } }, "meta": { "createdByUser": { "id": 235284, "username": "buki", "name": "Andreas Bauer", "links": { "alternate": { "href": "https://www.zotero.org/buki", "type": "text/html" } } }, "creatorSummary": "Cui and Potok", "parsedDate": "2006", "numChildren": 1 }, "data": { "key": "ZIUQ7HGU", "version": 1, "itemType": "conferencePaper", "title": "A Distributed Flocking Approach for Information Stream Clustering Analysis", "creators": [ { "creatorType": "author", "firstName": "Xiaohui", "lastName": "Cui" }, { "creatorType": "author", "firstName": "Thomas E.", "lastName": "Potok" } ], "abstractNote": "Intelligence analysts are currently overwhelmed with the amount of information streams generated everyday. There is a lack of comprehensive tool that can real-time analyze the information streams. Document clustering analysis plays an important role in improving the accuracy of information retrieval. However, most clustering technologies can only be applied for analyzing the static document collection because they normally require a large amount of computation resource and long time to get accurate result. It is very difficult to cluster a dynamic changed text information streams on an individual computer. Our early research has resulted in a dynamic reactive flock clustering algorithm which can continually refine the clustering result and quickly react to the change of document contents. This character makes the algorithm suitable for cluster analyzing dynamic changed document information, such as text information stream. Because of the decentralized character of this algorithm, a distributed approach is a very natural way to increase the clustering speed of the algorithm. 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