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Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins.", "publicationTitle": "BMC Bioinformatics", "publisher": "", "place": "", "date": "November 20, 2018", "volume": "19", "issue": "14", "section": "", "partNumber": "", "partTitle": "", "pages": "410", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "BMC Bioinformatics", "DOI": "10.1186/s12859-018-2378-9", "citationKey": "", "url": "https://doi.org/10.1186/s12859-018-2378-9", "accessDate": "2018-11-24T01:33:19Z", "PMID": "", "PMCID": "", "ISSN": "1471-2105", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "BioMed Central", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "EJUYWVX9" ], "relations": {}, "dateAdded": "2018-11-24T01:33:20Z", "dateModified": "2018-11-24T01:33:20Z" } }, { "key": "K2TKYLWK", "version": 930, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/K2TKYLWK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/K2TKYLWK", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/678222/items/BMSNQJHY", "type": "application/json" } }, "meta": { "createdByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "K2TKYLWK", "version": 930, "parentItem": "BMSNQJHY", "itemType": "attachment", "linkMode": "imported_file", "title": "Li et al. - 2018 - Deep Saliency With Channel-Wise Hierarchical Featu.pdf", "accessDate": "", "url": "", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Li et al. - 2018 - Deep Saliency With Channel-Wise Hierarchical Featu.pdf", "md5": "761561af7d26c28f08dca9991094eaf5", "mtime": 1540688284566, "tags": [], "relations": {}, "dateAdded": "2018-10-28T00:58:11Z", "dateModified": "2018-10-28T00:58:11Z" } }, { "key": "3KRL3EAZ", "version": 929, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/3KRL3EAZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/3KRL3EAZ", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/678222/items/876HP5SG", "type": "application/json" } }, "meta": { "createdByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "3KRL3EAZ", "version": 929, "parentItem": "876HP5SG", "itemType": "attachment", "linkMode": "imported_url", "title": "Zhang et al. - 2018 - Improve Network Embeddings with Regularization.pdf", "accessDate": "2018-10-28T00:51:17Z", "url": "http://jlu.myweb.cs.uwindsor.ca/n2v.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Zhang et al. - 2018 - Improve Network Embeddings with Regularization.pdf", "md5": "ba3021a199ce30de1765bd964473402f", "mtime": 1540687880720, "tags": [], "relations": {}, "dateAdded": "2018-10-28T00:51:17Z", "dateModified": "2018-10-28T00:51:21Z" } }, { "key": "876HP5SG", "version": 927, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/876HP5SG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/876HP5SG", "type": "text/html" } }, "meta": { "createdByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Zhang et al.", "parsedDate": "2018", "numChildren": 1 }, "data": { "key": "876HP5SG", "version": 927, "itemType": "conferencePaper", "title": "Improve Network Embeddings with Regularization", "creators": [ { "creatorType": "author", "firstName": "Yi", "lastName": "Zhang" }, { "creatorType": "author", "firstName": "Jianguo", "lastName": "Lu" }, { "creatorType": "author", "firstName": "Ofer", "lastName": "Shai" } ], "abstractNote": "Learning network representations is essential for many downstream tasks such as node classification, link prediction, and recommendation. Many algorithms derived from SGNS (skip-gram with negative sampling) have been proposed, such as LINE, DeepWalk, and node2vec. In this paper, we show that these algorithms suffer from norm convergence problem, and propose to use L2 regularization to rectify the problem. The proposed method improves the embeddings consistently. This is verified on seven different datasets with various sizes and structures. The best improvement is 46.41% for the task of node classification.", "proceedingsTitle": "Proceedings of the 27th ACM International Conference on Information and Knowledge Management - CIKM '18", "conferenceName": "the 27th ACM International Conference", "publisher": "ACM Press", "place": "Torino, Italy", "date": "2018", "eventPlace": "", "volume": "", "issue": "", "numberOfVolumes": "", "pages": "1643-1646", "series": "", "seriesNumber": "", "DOI": "10.1145/3269206.3269320", "ISBN": "978-1-4503-6014-2", "citationKey": "", "url": "http://dl.acm.org/citation.cfm?doid=3269206.3269320", "accessDate": "2018-10-28T00:51:20Z", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "Crossref", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [ "FBHR4SLU" ], "relations": {}, "dateAdded": "2018-10-28T00:51:20Z", "dateModified": "2018-10-28T00:51:20Z" } }, { "key": "M9J7K2DK", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/M9J7K2DK", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/M9J7K2DK", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Ezeife and Mutsuddy", "parsedDate": "2012-12", "numChildren": 0 }, "data": { "key": "M9J7K2DK", "version": 926, "itemType": "journalArticle", "title": "Towards Comparative Mining of Web Document Objects with NFA: WebOMiner System", "creators": [ { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" }, { "creatorType": "author", "firstName": "Titas", "lastName": "Mutsuddy" } ], "abstractNote": "The process of extracting comparative heterogeneous web content data which are derived and historical from related web pages is still at its infancy and not developed. Discovering potentially useful and previously unknown information or knowledge from web contents such as \"list all articles on 'Sequential Pattern Mining' written between 2007 and 2011 including title, authors, volume, abstract, paper, citation, year of publication,\" would require finding the schema of web documents from different web pages, performing web content data integration, building their virtual or physical data warehouse before web content extraction and mining from the database. This paper proposes a technique for automatic web content data extraction, the WebOMiner system, which models web sites of a specific domain like Business to Customer (B2C) web sites, as object oriented database schemas. Then, non-deterministic finite state automata (NFA) based wrappers for recognizing content types from this domain are built and used for extraction of related contents from data blocks into an integrated database for future second level mining for deep knowledge discovery.", "publicationTitle": "International Journal of Data Warehousing and Mining", "publisher": "", "place": "", "date": "OCT-DEC 2012", "volume": "8", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "1-21", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Int. J. Data Warehous. Min.", "DOI": "10.4018/jdwm.2012100101", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "1548-3924", "archive": "", "archiveLocation": "", "shortTitle": "Towards Comparative Mining of Web Document Objects with NFA", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000314401200001", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:20:50Z" } }, { "key": "7MIBWXKR", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/7MIBWXKR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/7MIBWXKR", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Ezeife and Chen", "parsedDate": "2004", "numChildren": 0 }, "data": { "key": "7MIBWXKR", "version": 926, "itemType": "book", "title": "Incremental mining of web sequential patterns using PLWAP tree on tolerance MinSupport", "creators": [ { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" }, { "creatorType": "author", "firstName": "M.", "lastName": "Chen" }, { "creatorType": "editor", "firstName": "J.", "lastName": "Bernardino" }, { "creatorType": "editor", "firstName": "B. C.", "lastName": "Desai" } ], "abstractNote": "This paper proposes an algorithm, PL4UP, which uses the PLWAP tree structure to incrementally update web sequential patterns. PL4UP initially builds a bigger PLWAP tree that includes all sequences in the database with a tolerance support, t, that is a fraction of the database minimum support, s. The position code features of the PLWAP tree are used to efficiently mine this tree to extract both current frequent and non-frequent sequences, which are likely to become frequent when the database is updated. This approach more quickly updates old frequent patterns without the need to re-scan the entire updated database.", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2004", "publisher": "Ieee Computer Soc", "place": "Los Alamitos", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "", "ISBN": "978-0-7695-2168-8", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000223083100051", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:20:39Z" } }, { "key": "TW4LM2XA", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/TW4LM2XA", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/TW4LM2XA", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Lu and Ezeife", "parsedDate": "2003", "numChildren": 0 }, "data": { "key": "TW4LM2XA", "version": 926, "itemType": "bookSection", "title": "Position coded pre-order linked WAP-tree for web log sequential pattern mining", "creators": [ { "creatorType": "author", "firstName": "Y.", "lastName": "Lu" }, { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" }, { "creatorType": "editor", "firstName": "K. Y.", "lastName": "Whang" }, { "creatorType": "editor", "firstName": "J.", "lastName": "Jeon" }, { "creatorType": "editor", "firstName": "K.", "lastName": "Shim" }, { "creatorType": "editor", "firstName": "J.", "lastName": "Srivastava" } ], "abstractNote": "Web access pattern tree algorithm mines web log access sequences by first storing the original web access sequence database on a prefix tree (WAP-tree). WAP-tree algorithm then mines frequent sequences from the WAP-tree by recursively re-constructing intermediate WAP-trees, starting with their suffix subsequences. This paper proposes an efficient approach for using the preorder linked WAP-trees with binary position codes assigned to each node, to mine frequent sequences, which eliminates the need to engage in numerous re-construction of intermediate WAP-trees during mining. Experiments show huge performance advantages for sequential mining using prefix linked WAP-tree technique.", "bookTitle": "Advances in Knowledge Discovery and Data Mining", "series": "", "seriesNumber": "", "volume": "2637", "numberOfVolumes": "", "edition": "", "date": "2003", "publisher": "Springer-Verlag Berlin", "place": "Berlin", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "337-349", "ISBN": "978-3-540-04760-5", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000184716000033", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:20:29Z" } }, { "key": "7XNZP2N7", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/7XNZP2N7", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/7XNZP2N7", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Ezeife and Monwar", "parsedDate": "2007", "numChildren": 0 }, "data": { "key": "7XNZP2N7", "version": 926, "itemType": "book", "title": "SSM : A frequent sequential data stream patterns miner", "creators": [ { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" }, { "creatorType": "author", "firstName": "Mostafa", "lastName": "Monwar" } ], "abstractNote": "Data stream applications like sensor network data, click stream data, have data arriving continuously at high speed rates and require online mining process capable of delivering current and near accurate results on demand without full access to all historical stored data. Frequent sequential mining is the process of discovering frequent sequential patterns in data sequences as found in applications like web log access sequences. Mining frequent sequential patterns on data stream applications contend with many challenges such as limited memory for unlimited data, inability of algorithms to scan infinitely flowing original dataset more than once and to deliver current and accurate result on demand. Existing work on mining frequent patterns on data streams are mostly for non-sequential patterns. This paper proposes SSM-Algorithm (Sequential Stream Mining-algorithm), that uses three types of data structures (D-List., PLWAP tree and FSP-tree) to handle the complexities of mining frequent sequential patterns in data streams. It summarizes frequency counts of items with the D-List, continuously builds PLWAP tree and mines frequent sequential patterns of batches of stream records, maintains mined frequent sequential patterns incrementally with FSP tree. The proposed algorithm can be deployed to analyze E-commerce data where the primary source of data is click stream data.", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2007", "publisher": "Ieee", "place": "New York", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "numPages": "", "ISBN": "978-1-4244-0705-7", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "SSM", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000249119800019", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:20:16Z" } }, { "key": "JQU6PR27", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/JQU6PR27", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/JQU6PR27", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Xu and Ezeife", "parsedDate": "2000", "numChildren": 0 }, "data": { "key": "JQU6PR27", "version": 926, "itemType": "bookSection", "title": "Maintaining horizontally partitioned warehouse views", "creators": [ { "creatorType": "author", "firstName": "M.", "lastName": "Xu" }, { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" }, { "creatorType": "editor", "firstName": "Y.", "lastName": "Kambayashi" }, { "creatorType": "editor", "firstName": "M.", "lastName": "Mohania" }, { "creatorType": "editor", "firstName": "A. M.", "lastName": "Tjoa" } ], "abstractNote": "Data warehouses usually store large amounts of information, representing an integration of base data from different data sources over a long time period. Aggregate views can be stored as a set of its horizontal fragments for the purposes of reducing warehouse query response time and maintenance cost. This paper proposes a scheme that efficiently maintains horizontally partitioned data warehouse views. Using the proposed scheme, only one view fragment holding the relevant subset of tuples of the view is accessed for each update. The scheme also includes an approach to reduce the refresh time for maintaining views that compute aggregate functions MIN and MAX.", "bookTitle": "Data Warehousing and Knowledge Discovery, Proceedings", "series": "", "seriesNumber": "", "volume": "1874", "numberOfVolumes": "", "edition": "", "date": "2000", "publisher": "Springer-Verlag Berlin", "place": "Berlin", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "126-133", "ISBN": "978-3-540-67980-6", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000174538800013", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:20:05Z" } }, { "key": "CVTSIH9L", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/CVTSIH9L", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/CVTSIH9L", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Mabroukeh and Ezeife", "parsedDate": "2009", "numChildren": 0 }, "data": { "key": "CVTSIH9L", "version": 926, "itemType": "bookSection", "title": "Semantic-rich Markov Models for Web Prefetching", "creators": [ { "creatorType": "author", "firstName": "Nizar R.", "lastName": "Mabroukeh" }, { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" }, { "creatorType": "editor", "firstName": "Y.", "lastName": "Saygin" }, { "creatorType": "editor", "firstName": "J. X.", "lastName": "Yu" }, { "creatorType": "editor", "firstName": "H.", "lastName": "Kargupta" }, { "creatorType": "editor", "firstName": "W.", "lastName": "Wang" }, { "creatorType": "editor", "firstName": "S.", "lastName": "Ranka" }, { "creatorType": "editor", "firstName": "P. S.", "lastName": "Yu" }, { "creatorType": "editor", "firstName": "X. D.", "lastName": "Wu" } ], "abstractNote": "Domain knowledge for web applications is currently being made available as domain ontology with the advent of the semantic web, in which semantics govern relationships among objects of interest (e.g., commercial items to be purchased in an e-Commerce web site). Our earlier work proposed to integrate semantic information into all phases of the web usage mining process, for an intelligent semantics-aware web usage mining framework. There are ways to integrate semantic information into Markov models used in the third phase for next page request prediction. Semantic information is combined with the transition probability matrix of a Markov model. This way, it provides a low order Markov model with intelligent accurate predictions and less complexity than higher order models, also solving the problem of contradicting prediction. This paper proposes to use semantic information to prune states in Selective Markov models SMM, semantic information can lead to context-aware higher order Markov models with about 16% less space complexity.", "bookTitle": "2009 Ieee International Conference on Data Mining Workshops (icdmw 2009)", "series": "", "seriesNumber": "", "volume": "", "numberOfVolumes": "", "edition": "", "date": "2009", "publisher": "Ieee", "place": "New York", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "465-470", "ISBN": "978-1-4244-5384-9", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000290247100075", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:19:54Z" } }, { "key": "DSDJNCM2", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/DSDJNCM2", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/DSDJNCM2", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Ezeife and Zhang", "parsedDate": "2009", "numChildren": 0 }, "data": { "key": "DSDJNCM2", "version": 926, "itemType": "bookSection", "title": "TidFP: Mining Frequent Patterns in Different Databases with Transaction ID", "creators": [ { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" }, { "creatorType": "author", "firstName": "Dan", "lastName": "Zhang" }, { "creatorType": "editor", "firstName": "T. B.", "lastName": "Pedersen" }, { "creatorType": "editor", "firstName": "M. K.", "lastName": "Mohania" }, { "creatorType": "editor", "firstName": "A. M.", "lastName": "Tjoa" } ], "abstractNote": "Since transaction identifiers (ids) are unique and would not; usually be frequent, mining frequent patterns with transaction ids, showing records they occurred in, provides an efficient way to mine frequent patterns in many types of databases including multiple tabled and distributed databases. Existing work have not focused on mining frequent patterns with the transaction ids they occurred in. Many applications require finding strong associations between transaction id (e.g., certain drug) and the itemsets (e.g., certain adverse effects) to help deduce some pertinent lacking information (like how many people use this product in total) and information (like how many people have the adverse effects). This paper proposes a set of algorithms TidiFPs, for mining frequent patterns with their transaction ids in a single transaction database, in a, multiple tabled database, and in a distributed database. The proposed technique scans the database records only once even with level-wise Apriori-based mining techniques, stores frequent 1-items with their transaction id bitmap, outperforms traditional approaches and is extendible to other tree-based mining techniques as well as sequential mining.", "bookTitle": "Data Warehousing and Knowledge Discovery, Proceedings", "series": "", "seriesNumber": "", "volume": "5691", "numberOfVolumes": "", "edition": "", "date": "2009", "publisher": "Springer-Verlag Berlin", "place": "Berlin", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "125-137", "ISBN": "978-3-642-03729-0", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "TidFP", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000273239000011", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:19:41Z" } }, { "key": "WRFFTMSC", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/WRFFTMSC", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/WRFFTMSC", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Rahman et al.", "parsedDate": "2010", "numChildren": 0 }, "data": { "key": "WRFFTMSC", "version": 926, "itemType": "bookSection", "title": "WiFi Miner: An Online Apriori-Infrequent Based Wireless Intrusion System", "creators": [ { "creatorType": "author", "firstName": "Ahmedur", "lastName": "Rahman" }, { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" }, { "creatorType": "author", "firstName": "A. K.", "lastName": "Aggarwal" }, { "creatorType": "editor", "firstName": "M. M.", "lastName": "Gaber" }, { "creatorType": "editor", "firstName": "R. R.", "lastName": "Vatsavai" }, { "creatorType": "editor", "firstName": "O. A.", "lastName": "Omitaomu" }, { "creatorType": "editor", "firstName": "J.", "lastName": "Gama" }, { "creatorType": "editor", "firstName": "N. V.", "lastName": "Chawla" }, { "creatorType": "editor", "firstName": "A. R.", "lastName": "Ganguly" } ], "abstractNote": "Intrusion detection in wireless networks has become a vital part in wireless network security systems with wide spread use of Wireless Local Area Networks (WLAN). Currently, almost all devices are Wi-Fi (Wireless Fidelity) capable and can access WLAN. This paper proposes an Intrusion Detection System, WiFi Miner, which applies an infrequent pattern association rule mining Apriori technique to wireless network packets captured through hardware sensors for purposes of real time detection of intrusive or anomalous packets. Contributions of the proposed system includes effectively adapting an efficient data mining association rule technique to important problem of intrusion detection in a wireless network environment using hardware sensors, providing a solution that eliminates the need for hard-to-obtain training data in this environment, providing increased intrusion detection rate and reduction of false alarms. The proposed system, WiFi Miner solution approach is to find frequent and infrequent patterns on pre-processed wireless connection records using infrequent pattern finding Apriori algorithm proposed by this paper. The proposed Online Apriori-Infrequent algorithm improves the join and prune step of the traditional Apriori algorithm with a rule that avoids joining itemsets not likely to produce frequent itemsets as their results, there by improving efficiency and run times significantly. An anomaly score is assigned to each packet (record) based on whether the record has more frequent or infrequent patterns. Connection records with positive anomaly scores have more infrequent patterns than frequent patterns and are considered anomalous packets.", "bookTitle": "Knowledge Discovery from Sensor Data", "series": "", "seriesNumber": "", "volume": "5840", "numberOfVolumes": "", "edition": "", "date": "2010", "publisher": "Springer-Verlag Berlin", "place": "Berlin", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "76-93", "ISBN": "978-3-642-12518-8", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "WiFi Miner", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000280082600005", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:19:36Z" } }, { "key": "LBRRK7U6", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/LBRRK7U6", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/LBRRK7U6", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Ezeife", "parsedDate": "2001-02", "numChildren": 0 }, "data": { "key": "LBRRK7U6", "version": 926, "itemType": "journalArticle", "title": "Selecting and materializing horizontally partitioned warehouse views", "creators": [ { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" } ], "abstractNote": "Data warehouse views typically store large aggregate tables based on a subset of dimension attributes of the main data warehouse fact table. Aggregate views can be stored as 2 \" subviews of a data cube with n attributes. Methods have been proposed for selecting only some of the data cube views to materialize in order to speed up query response time, accommodate storage space constraint and reduce warehouse maintenance cost. This paper proposes a method for selecting and materializing views, which selects and horizontally fragments a view, recomputes the size of the stored partitioned view while deciding further views to select. (C) 2001 Elsevier Science B.V. All rights reserved.", "publicationTitle": "Data & Knowledge Engineering", "publisher": "", "place": "", "date": "FEB 2001", "volume": "36", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "185-210", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Data Knowl. Eng.", "DOI": "10.1016/S0169-023X(00)00044-6", "citationKey": "", "url": "", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "0169-023X", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000166389100003", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:19:22Z" } }, { "key": "PBPUBCVU", "version": 926, "library": { "type": "group", "id": 678222, "name": "UWin-Computer Sciences", "links": { "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/678222/items/PBPUBCVU", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/uwin-computer_sciences/items/PBPUBCVU", "type": "text/html" } }, "meta": { "createdByUser": { "id": 4614550, "username": "tschumil", "name": "", "links": { "alternate": { "href": "https://www.zotero.org/tschumil", "type": "text/html" } } }, "lastModifiedByUser": { "id": 5110, "username": "copystar", "name": "Mita Williams", "links": { "alternate": { "href": "https://www.zotero.org/copystar", "type": "text/html" } } }, "creatorSummary": "Li and Ezeife", "parsedDate": "2006", "numChildren": 0 }, "data": { "key": "PBPUBCVU", "version": 926, "itemType": "bookSection", "title": "Cleaning web pages for effective web content mining", "creators": [ { "creatorType": "author", "firstName": "Jing", "lastName": "Li" }, { "creatorType": "author", "firstName": "C. I.", "lastName": "Ezeife" }, { "creatorType": "editor", "firstName": "S.", "lastName": "Bressan" }, { "creatorType": "editor", "firstName": "J.", "lastName": "Kung" }, { "creatorType": "editor", "firstName": "R.", "lastName": "Wagner" } ], "abstractNote": "Classifying and mining noise-free web pages will improve on accuracy of search results as well as search speed, and may benefit web-page organization applications (e.g., keyword-based search engines and taxonomic web page categorization applications). Noise on web pages are irrelevant to the main content on the web pages being mined, and include advertisements, navigation bar, and copyright notices. The few existing work on web page cleaning detect noise blocks with exact matching contents but are weak at detecting near duplicate blocks, characterized by items like navigation bars. This paper proposes a system, WebPageCleaner, for eliminating noise blocks from web pages for purposes of improving the accuracy and efficiency of web content mining. A vision-based technique is employed for extracting blocks from web pages. Then, relevant web page blocks are identified as those with high importance level by analyzing such physical features of the blocks as the block location, percentage of web links on the block, and level of similarity of block contents to other blocks. Important blocks are exported to be used for web content mining using Naive Bayes text classification. Experiments show that WebPageCleaner leads to a more accurate and efficient web page classification results than comparable existing approaches.", "bookTitle": "Database and Expert Systems Applications, Proceedings", "series": "", "seriesNumber": "", "volume": "4080", "numberOfVolumes": "", "edition": "", "date": "2006", "publisher": "Springer-Verlag Berlin", "place": "Berlin", "originalDate": "", "originalPublisher": "", "originalPlace": "", "format": "", "pages": "560-571", "ISBN": "978-3-540-37871-6", "DOI": "", "citationKey": "", "url": "", "accessDate": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "English", "libraryCatalog": "Web of Science", "callNumber": "", "rights": "", "extra": "WOS:000241155300055", "tags": [], "collections": [], "relations": {}, "dateAdded": "2018-01-09T20:53:07Z", "dateModified": "2018-10-15T01:19:07Z" } } ]