02174nas a2200229 4500008004100000245005600041210005500097260003400152520148700186653002001673653002301693653002601716653001801742653002201760653001701782100002001799700001701819700002001836700003201856700001601888856004001904 2015 eng d00aIntent Classification of Short-Text on Social Media0 aIntent Classification of ShortText on Social Media aChengdu, ChinabIEEEc12/20153 aSocial media platforms facilitate the emergence of citizen communities that discuss real-world events. Their content reflects a variety of intent ranging from social good (e.g., volunteering to help) to commercial interest (e.g., criticizing product features). Hence, mining intent from social data can aid in filtering social media to support organizations, such as an emergency management unit for resource planning. However, effective intent mining is inherently challenging due to ambiguity in interpretation, and sparsity of relevant behaviors in social data. In this paper, we address the problem of multiclass classification of intent with a use-case of social data generated during crisis events. Our novel method exploits a hybrid feature representation created by combining top-down processing using knowledge-guided patterns with bottom-up processing using a bag-of-tokens model. We employ pattern-set creation from a variety of knowledge sources including psycholinguistics to tackle the ambiguity challenge, social behavior about conversations to enrich context, and contrast patterns to tackle the sparsity challenge. Our results show a significant absolute gain up to 7% in the F1 score relative to a baseline using bottom-up processing alone, within the popular multiclass frameworks of One-vs-One and One-vs-All. Intent mining can help design efficient cooperative information systems between citizens and organizations for serving organizational information needs.10aContrast mining10aCrisis Informatics10aDeclarative Knowledge10aIntent Mining10aPsycholinguistics10aSocial Media1 aPurohit, Hemant1 aDong, Guozhu1 aShalin, Valerie1 aThirunarayan, Krishnaprasad1 aSheth, Amit uhttp://knoesis.wright.edu/node/217202169nas a2200205 4500008004100000245006800041210006700109260001200176300001400188490000700202520150500209653004001714653001601754653001901770653003401789653003401823100001701857700002601874856006301900 2015 eng d00aPattern-Aided Regression Modeling and Prediction Model Analysis0 aPatternAided Regression Modeling and Prediction Model Analysis c11/2015 a2452-24650 v273 aThis paper first introduces pattern aided regression (PXR) models, a new type of regression models designed to represent accurate and interpretable prediction models. This was motivated by two observations: (1) Regression modeling applications often involve complex diverse predictor-response relationships, which occur when the optimal regression models (of given regression model type) fitting two or more distinct logical groups of data are highly different. (2) State-of-the-art regression methods are often unable to adequately model such relationships. This paper defines PXR models using several patterns and local regression models, which respectively serve as logical and behavioral characterizations of distinct predictor-response relationships. The paper also introduces a contrast pattern aided regression (CPXR) method, to build accurate PXR models. In experiments, the PXR models built by CPXR are very accurate in general, often outperforming state-of-the-art regression methods by big margins. Usually using (a) around seven simple patterns and (b) linear local regression models, those PXR models are easy to interpret; in fact, their complexity is just a bit higher than that of (piecewise) linear regression models and is significantly lower than that of traditional ensemble based regression models. CPXR is especially effective for high-dimensional data. The paper also discusses how to use CPXR methodology for analyzing prediction models and correcting their prediction errors.10aCorrelation and regression analysis10aData Mining10aerror analysis10amining methods and algorithms10amodel validation and analysis1 aDong, Guozhu1 aTaslimitehrani, Vahid uhttp://knoesis.wright.edu/library/resource.php%3Fid%3D203801876nas a2200181 4500008004100000245013200041210006900173260003000242300001200272520120100284653002801485653002401513653002401537653002701561100002601588700001701614856006301631 2014 eng d00aA New CPXR Based Logistic Regression Method and Clinical Prognostic Modeling Results Using the Method on Traumatic Brain Injury0 aNew CPXR Based Logistic Regression Method and Clinical Prognosti aBoca Raton, FloridabIEEE a283-2903 aPrognostic modeling is central to medicine, as it is often used to predict patients' outcome and response to treatments and to identify important medical risk factors. Logistic regression is one of the most used approaches for clinical prediction modeling. Traumatic brain injury (TBI) is an important public health issue and a leading cause of death and disability worldwide. In this study, we adapt CPXR (Contrast Pattern Aided Regression, a recently introduced regression method), to develop a new logistic regression method called CPXR(Log), for general binary outcome prediction (including prognostic modeling), and we use the method to carry out prognostic modeling for TBI using admission time data. The models produced by CPXR(Log) achieved AUC as high as 0.93 and specificity as high as 0.97, much better than those reported by previous studies. Our method produced interpretable prediction models for diverse patient groups for TBI, which show that different kinds of patients should be evaluated differently for TBI outcome prediction and the odds ratios of some predictor variables differ significantly from those given by previous studies; such results can be valuable to physicians.10acontrast pattern mining10aLogistic regression10aPrognostic modeling10aTraumatic brain injury1 aTaslimitehrani, Vahid1 aDong, Guozhu uhttp://knoesis.wright.edu/library/resource.php%3Fid%3D202201157nas a2200133 4500008004100000245003600041210003600077260003300113520078400146100001600930700002000946700001700966856004000983 2013 eng d00aLogical Linked Data Compression0 aLogical Linked Data Compression aMontpellier, Francec05/20133 aLinked data has experienced accelerated growth in recent years. With the continuing proliferation of structured data, demand for RDF compression is becoming increasingly important. In this study, we introduce a novel lossless compression technique for RDF datasets, called Rule Based Compression (RB Compression) that compresses datasets by generating a set of new logical rules from the dataset and removing triples that can be inferred from these rules. Unlike other compression techniques, our approach not only takes advantage of syntactic verbosity and data redundancy but also utilizes semantic associations present in the RDF graph. Depending on the nature of the dataset, our system is able to prune more than 50% of the original triples without affecting data integrity.1 aJoshi, Amit1 aHitzler, Pascal1 aDong, Guozhu uhttp://knoesis.wright.edu/node/246101976nas a2200277 4500008004100000245008900041210006900130260001200199300001200211490000700223520116400230653001601394653003301410653003901443653002501482100001401507700001901521700001601540700001701556700001901573700001301592700001501605700001801620700002001638856004001658 2013 eng d00aMining Effective Multi-Segment Sliding Window for Pathogen Incidence Rate Prediction0 aMining Effective MultiSegment Sliding Window for Pathogen Incide c09/2013 a425-4440 v873 aPathogen incidence rate prediction, which can be considered as time series modeling, is an important task for infectious disease incidence rate prediction and for public health. This paper investigates applying a genetic computation technique, namely GEP, for pathogen incidence rate prediction. To overcome the shortcomings of traditional sliding windows in GEP based time series modeling, the paper introduces the problem of mining effective sliding window, for discovering optimal sliding windows for building accurate prediction models. To utilize the periodical characteristic of pathogen incidence rates, a multi-segment sliding window consisting of several segments from different periodical intervals is proposed and used. Since the number of such candidate windows is still very large, a heuristic method is designed for enumerating the candidate effective multi-segment sliding windows. Moreover, methods to find the optimal sliding window and then produce a mathematical model based on that window are proposed. A performance study on real-world datasets shows that the techniques are effective and efficient for pathogen incidence rate prediction.10aData Mining10aMulti-segment sliding window10aPathogen incidence rate prediction10aTime series modeling1 aDuan, Lei1 aTang, Changjie1 aLi, Xiasong1 aDong, Guozhu1 aWang, Xianming1 aZuo, Jie1 aJiang, Min1 aLi, Zhongqiao1 aZhang, Yongqing uhttp://knoesis.wright.edu/node/246200431nas a2200133 4500008004100000245007000041210006900111100001400180700001900194700001700213700001400230700001300244856004000257 2012 eng d00aSurvey of Emerging Pattern based Contrast Mining and Applications0 aSurvey of Emerging Pattern based Contrast Mining and Application1 aDuan, Lei1 aTang, Changjie1 aDong, Guozhu1 aYang, Nin1 aGou, Chi uhttp://knoesis.wright.edu/node/159400379nas a2200097 4500008004100000245010000041210006900141100001400210700001700224856004000241 2012 eng d00aUse Attribute Behavior Diversity to Build Accurate Decision Tree Committees for Microarray Data0 aUse Attribute Behavior Diversity to Build Accurate Decision Tree1 aHan, Qian1 aDong, Guozhu uhttp://knoesis.wright.edu/node/159500405nas a2200109 4500008004100000245009200041210006900133260002100202100001500223700001700238856004000255 2011 eng d00aDiscovering Dynamic Logical Blog Communities Based on Their Distinct Interest Profiles.0 aDiscovering Dynamic Logical Blog Communities Based on Their Dist aBarcelona, Spain1 aFore, Neil1 aDong, Guozhu uhttp://knoesis.wright.edu/node/119600517nas a2200133 4500008004100000245007300041210006900114260009600183100001700279700001700296700001300313700001700326856004000343 2011 eng d00aAn Equivalence Class Based Clustering Algorithm for Categorical Data0 aEquivalence Class Based Clustering Algorithm for Categorical Dat aBarcelona, SpainbInternational Conference on Advances in Information Mining and Management1 aLiu, Qingbao1 aWang, Wanjun1 aDeng, Su1 aDong, Guozhu uhttp://knoesis.wright.edu/node/119700404nas a2200109 4500008004100000245008000041210006900121260002900190100001700219700001800236856004000254 2011 eng d00aOverview of Contrast Data Mining as a Field and Preview of an Upcoming Book0 aOverview of Contrast Data Mining as a Field and Preview of an Up aLas Vegas, NVbICDM 20111 aDong, Guozhu1 aBailey, James uhttp://knoesis.wright.edu/node/185500576nas a2200169 4500008004100000245009400041210006900135260001700204490000900221653002400230653004600254653001500300653002100315100001700336700001300353856004000366 2010 eng d00aAnalyzing and Tracking Weblog Communities Using Discriminative Collection Representatives0 aAnalyzing and Tracking Weblog Communities Using Discriminative C aBethesda, MD0 v600710aBehavioral Modeling10aDiscriminative Collection Representatives10aPrediction10aSocial Computing1 aDong, Guozhu1 aSa, Ting uhttp://knoesis.wright.edu/node/105201971nas a2200193 4500008004100000245012100041210006900162520130700231653003601538653002601574653002101600653001501621653002601636653002601662100001401688700001801702700001701720856004001737 2010 eng d00aA Clustering Comparison Measure Using Density Profiles and its Application to the Discovery of Alternate Clusterings0 aClustering Comparison Measure Using Density Profiles and its App3 aData clustering is a fundamental and very popular method of data analysis. Its subjective nature, however, means that different clustering algorithms or different parameter settings can produce widely varying and sometimes conflicting results. This has led to the use of clustering comparison measures to quantify the degree of similarity between alternative clusterings. Existing measures, though, can be limited in their ability to assess similarity and sometimes generate unintuitive results. They also cannot be applied to compare clusterings which contain different data points, an activity which is important for scenarios such as data stream analysis. In this paper, we introduce a new clustering similarity measure, known as ADCO, which aims to address some limitations of existing measures, by allowing greater flexibility of comparison via the use of density profiles to characterize a clustering. In particular, it adopts a 'data mining style' philosophy to clustering comparison, whereby two clusterings are considered to be more similar, if they are likely to give rise to similar types of prediction models. Furthermore, we show that this new measure can be applied as a highly effective objective function within a new algorithm, known as MAXIMUS, for generating alternate clusterings.
10aalternate clustering algorithms10aalternate clusterings10acluster analysis10aclustering10aclustering comparison10aclustering similarity1 aBae, Eric1 aBailey, James1 aDong, Guozhu uhttp://knoesis.wright.edu/node/163901715nas a2200265 4500008004100000245006400041210006300105520096000168653002201128653001601150653001901166653001801185653001701203653002201220653002001242653001801262653002301280653001901303653001601322653001501338100001801353700002101371700001701392856004001409 2010 eng d00aLogical Queries over Views: Decidability and Expressiveness0 aLogical Queries over Views Decidability and Expressiveness3 aWe study the problem of deciding the satisfiability of first-order logic queries over views, with our aim to delimit the boundary between the decidable and the undecidable fragments of this language. Views currently occupy a central place in database research due to their role in applications such as information integration and data warehousing. Our main result is the identification of a decidable class of first-order queries over unary conjunctive views that general the decidability of the classical class of first-order sentences over unary relations known as the Lowenheim class. We then demonstrate how various extensions of this class lead to undecidability and also provide some expressivity results. Besides its theoretical interest, our new decidable class is potentially interesting for use in applications such as deciding implication of complex dependencies, analysis of a restricted class of active database rules, and ontology reasoning.10aconjunctive query10acontainment10adatabase query10adatabase view10adecidability10afirst-order logic10aLowenheim class10amonadic logic10aontology reasoning10aSatisfiability10aunary logic10aunary view1 aBailey, James1 aWidjaja, Anthony1 aDong, Guozhu uhttp://knoesis.wright.edu/node/162601437nas a2200229 4500008004100000245007000041210006900111260001200180300001300192490000700205520075300212653002700965653003900992653002101031653002801052100001901080700001701099700001501116700001801131700001801149856004001167 2010 eng d00aPattern Space Maintenance for Data Updates and Interactive Mining0 aPattern Space Maintenance for Data Updates and Interactive Minin c08/2010 a 282-3170 v263 aThis paper addresses the incremental and decremental maintenance of the frequent pattern space. We conduct an in-depth investigation on how the frequent pattern space evolves under both incremental and decremental updates. Based on the evolution analysis, a new data structure, Generator-Enumeration Tree (GE-tree), is developed to facilitate the maintenance of the frequent pattern space. With the concept of GE-tree, we propose two novel algorithms, Pattern Space Maintainer+ (PSM+) and Pattern Space Maintainer- (PSM-), for the incremental and decremental maintenance of frequent patterns. Experimental results demonstrate that the proposed algorithms, on average, outperform the representative state-of-the-art methods by an order of magnitude.10aData mining algorithms10adata update and interactive mining10afrequent pattern10aincremental maintenance1 aFeng, Mengling1 aDong, Guozhu1 aLi, Jinyan1 aTan, Yap-Peng1 aWong, Limsoon uhttp://knoesis.wright.edu/node/163800341nas a2200109 4500008004100000245004700041210004700088100001700135700001800152700002100170856004000191 2010 eng d00aUnary First Order Logic Queries Over Views0 aUnary First Order Logic Queries Over Views1 aDong, Guozhu1 aBailey, James1 aWidjaja, Anthony uhttp://knoesis.wright.edu/node/148501240nas a2200121 4500008004100000245007500041210006900116260001900185520084000204100001701044700001701061856004001078 2009 eng d00aA Contrast Pattern Based Clustering Quality Index for Categorical Data0 aContrast Pattern Based Clustering Quality Index for Categorical aMiami, Florida3 aSince clustering is unsupervised and highly explorative, clustering validation (i.e. assessing the quality of clustering solutions) has been an important and long standing research problem. Existing validity measures have significant shortcomings. This paper proposes a novel Contrast Pattern based Clustering Quality index (CPCQ) for categorical data, by utilizing the quality and diversity of the contrast patterns (CPs) which contrast the clusters in clusterings. High quality CPs can characterize clusters and discriminate them against each other. Experiments show that the CPCQ index (1) can recognize that expert-determined classes are the best clusters for many datasets from the UCI repository; (2) does not give inappropriate preference to larger number of clusters; (3) does not require a user to provide a distance function.1 aLiu, Qingbao1 aDong, Guozhu uhttp://knoesis.wright.edu/node/105300295nam a2200097 4500008004100000245004200041210004200083100001700125700001500142856004000157 2009 eng d00aEmerging Pattern Based Classification0 aEmerging Pattern Based Classification1 aDong, Guozhu1 aLi, Jinyan uhttp://knoesis.wright.edu/node/243000255nas a2200097 4500008004100000245002200041210002200063100001500085700001700100856004000117 2009 eng d00aEmerging Patterns0 aEmerging Patterns1 aLi, Jinyan1 aDong, Guozhu uhttp://knoesis.wright.edu/node/147500457nam a2200109 4500008004100000245014500041210006900186100001700255700001800272700001700290856004000307 2009 eng d00aEvaluation of Inter Laboratory and Cross Platform Concordance of DNA Microarrays through Discriminating Genes and Classifier Transferability0 aEvaluation of Inter Laboratory and Cross Platform Concordance of1 aMao, Shihong1 aWang, Chalres1 aDong, Guozhu uhttp://knoesis.wright.edu/node/201101845nas a2200205 4500008004100000245003900041210003900080520120800119653003601327653002101363653002401384653003801408653003701446653003501483653003701518653001101555100001701566700001601583856004001599 2009 eng d00aIncremental Computation of Queries0 aIncremental Computation of Queries3 aA view on a database is defined by a query over the database. When the database is updated, the value of the view (namely the answer to the query) will likely change. The computation of the new answer to the query using the old answer is called incremental query computation or incremental view maintenance. Incremental computation is typically performed by identifying the part in the old answer that need to be removed, and the part in the new answer that need to be added. Incremental computation is desirable when it is much more efficient than a re-computation of the query. Efficiency can be measured by computation time, storage space, or query language desirability/availability, etc. Incremental computation algorithms could use auxiliary relations (in addition to the query answer), which also need to be incrementally computed. Two query languages can be involved for the incremental query computation problem. One is used for defining the view to be maintained, and the other for describing the incremental computation algorithm. For relational databases, the two languages can be relational algebra, SQL, nested relational algebra, Datalog, SQL embedded in a host programming language, etc.10aComputer Communication Networks10acomputer imaging10adatabase management10aInformation Storage and Retrieval10ainformation systems applications10aMultimedia Information Systems10apattern recognition and graphics10avision1 aDong, Guozhu1 aSu, Jianwen uhttp://knoesis.wright.edu/node/148401234nas a2200133 4500008004100000245004700041210004600088520085700134100001500991700001801006700001901024700001701043856004001060 2009 eng d00aMaintenance of Frequent Patterns: A Survey0 aMaintenance of Frequent Patterns A Survey3 aThis chapter surveys the maintenance of frequent patterns in transaction datasets. It is written to be accessible to researchers familiar with the field of frequent pattern mining. The frequent pattern main-tenance problem is summarized with a study on how the space of frequent patterns evolves in response to data updates. This chapter focuses on incremental and decremental maintenance. Four major types of maintenance algorithms are studied: Apriori-based, partition-based, prefix-tree-based, and concise-representation-based algorithms. The authors study the advantages and limitations of these algorithms from both the theoretical and experimental perspectives. Possible solutions to certain limitations are also proposed. In addition, some potential research opportunities and emerging trends in frequent pat-tern maintenance are also discussed.1 aLi, Jinyan1 aWong, Limsoon1 aFeng, Mengling1 aDong, Guozhu uhttp://knoesis.wright.edu/node/205702170nas a2200133 4500008004100000245004100041210004100082520180700123100001701930700001601947700001801963700001501981856004001996 2009 eng d00aMining Conditional Contrast Patterns0 aMining Conditional Contrast Patterns3 aThis chapter considers the problem of 'conditional contrast pattern mining.' It is related to contrast mining, where one considers the mining of patterns/models that contrast two or more datasets, classes, conditions, time periods, and so forth. Roughly speaking, conditional contrasts capture situations where a small change in patterns is associated with a big change in the matching data of the patterns. More precisely, a conditional contrast is a triple (B, F_{1}, F_{2}) of three patterns; B is the condition/context pattern of the conditional contrast, and F_{1} and F_{2} are the contrasting factors of the conditional contrast. Such a conditional contrast is of interest if the difference between F_{1} and F_{2} as itemsets is relatively small, and the difference between the corresponding matching dataset of B∪F_{1} and that of B∪F_{2 is relatively large. It offers insights on 'discriminating' patterns for a given condition B. Conditional contrast mining is related to frequent pattern mining and analysis in general, and to the mining and analysis of closed pattern and minimal generators in particular. It can also be viewed as a new direction for the analysis (and mining) of frequent patterns. After formalizing the concepts of conditional contrast, the chapter will provide some theoretical results on conditional contrast mining. These results (i) relate conditional contrasts with closed patterns and their minimal generators, (ii) provide a concise representation for conditional contrasts, and (iii) establish a so-called dominance-beam property. An efficient algorithm will be proposed based on these results, and experiment results will be reported. Related works will also be discussed.1 aDong, Guozhu1 aLiu, Guimei1 aWong, Limsoon1 aLi, Jinyan uhttp://knoesis.wright.edu/node/205802464nas a2200181 4500008004100000245007400041210006900115520189200184653003202076653001902108653002002127653002902147653001602176100001902192700001402211700001702225856004002242 2009 eng d00aMining Disease State Converters for Medical Intervention of Diseases.0 aMining Disease State Converters for Medical Intervention of Dise3 aIn applications such as gene therapy and drug design, a key goal is to convert the disease state of diseased objects from an undesirable state into a desirable one. Such conversions may be achieved by changing the values of some attributes of the objects. For example, in gene therapy one may convert cancerous cells to normal ones by changing some genes' expression level from low to high or from high to low. In this paper, we define the disease state conversion problem as the discovery of disease state converters; a disease state converter is a small set of attribute value changes that may change an object's disease state from undesirable into desirable. We consider two variants of this problem: personalized disease state converter mining mines disease state converters for a given individual patient with a given disease, and universal disease state converter mining mines disease state converters for all samples with a given disease. We propose a DSCMiner algorithm to discover small and highly effective disease state converters. Since real-life medical experiments on living diseased instances are expensive and time consuming, we use classifiers trained from the datasets of given diseases to evaluate the quality of discovered converter sets. The effectiveness of a disease state converter is measured by the percentage of objects that are successfully converted from undesirable state into desirable state as deemed by state-of-the-art classifiers. We use experiments to evaluate the effectiveness of our algorithm and to show its effectiveness. We also discuss possible research directions for extensions and improvements. We note that the disease state conversion problem also has applications in customer retention, criminal rehabilitation, and company turn-around, where the goal is to convert class membership of objects whose class is an undesirable class.10aClass membership conversion10aClassification10aContrast mining10aDisease state conversion10aDrug design1 aTang, Changjie1 aDuan, Lei1 aDong, Guozhu uhttp://knoesis.wright.edu/node/144600376nas a2200121 4500008004100000245005300041210005300094100001700147700002100164700001500185700001400200856004000214 2008 eng d00aMining Sequence Classifiers for Early Prediction0 aMining Sequence Classifiers for Early Prediction1 aDong, Guozhu1 aXing, Zhengzheng1 aYu, Philip1 aPei, Jian uhttp://knoesis.wright.edu/node/107900430nas a2200121 4500008004100000245010000041210006900141100001300210700001700223700001700240700001100257856004000268 2008 eng d00aSemantic Knowledge Facilities for a Web-based Recipe Database System Supporting Personalization0 aSemantic Knowledge Facilities for a Webbased Recipe Database Sys1 aLi, Qing1 aWang, Liping1 aDong, Guozhu1 aLi, Yu uhttp://knoesis.wright.edu/node/148300394nas a2200133 4500008004100000245005400041210005400095100001700149700001300166700001300179700001700192700001100209856004000220 2008 eng d00aSubstructure Similarity Search in Chinese Recipes0 aSubstructure Similarity Search in Chinese Recipes1 aDong, Guozhu1 aYang, Yu1 aLi, Qing1 aWang, Liping1 aLi, Na uhttp://knoesis.wright.edu/node/108000500nam a2200133 4500008004100000245013600041210006900177100001600246700001700262700001300279700002000292700001400312856004000326 2007 eng d00aAdvances in Data and Web Management: Proceedings of the Joint International ApWeb/WAIM Conference on Web-Age Information Management0 aAdvances in Data and Web Management Proceedings of the Joint Int1 aLin, Xuemin1 aDong, Guozhu1 aYang, Yu1 aYu, Jeffrey, Xu1 aWang, Wei uhttp://knoesis.wright.edu/node/201300398nas a2200109 4500008004100000245007500041210006900116100001900185700002700204700001700231856004000248 2007 eng d00aEfficient Computation of Iceberg Cubes by Bounding Aggregate Functions0 aEfficient Computation of Iceberg Cubes by Bounding Aggregate Fun1 aZhang, Xiuzhen1 aChou, Pauline, Lienhua1 aDong, Guozhu uhttp://knoesis.wright.edu/node/148900479nas a2200133 4500008004100000245010800041210006900149100001500218700001800233700001800251700001900269700001700288856004000305 2007 eng d00aEvolution and Maintenance of Frequent Pattern Space When Transactions Are Removed. Proceedings of PAKDD0 aEvolution and Maintenance of Frequent Pattern Space When Transac1 aLi, Jinyan1 aWong, Limsoon1 aTan, Yap-Peng1 aFeng, Mengling1 aDong, Guozhu uhttp://knoesis.wright.edu/node/111500387nas a2200109 4500008004100000245007600041210006900117100001700186700001600203700001800219856004000237 2007 eng d00aMining Minimal Distinguishing Subsequence Patterns with Gap Constraints0 aMining Minimal Distinguishing Subsequence Patterns with Gap Cons1 aDong, Guozhu1 aJi, Xiaonan1 aBailey, James uhttp://knoesis.wright.edu/node/149000260nam a2200097 4500008004100000245002500041210002500066100001400091700001700105856004000122 2007 eng d00aSequence Data Mining0 aSequence Data Mining1 aPei, Jian1 aDong, Guozhu uhttp://knoesis.wright.edu/node/201400442nas a2200109 4500008004100000245012300041210006900164100001700233700001800250700002400268856004000292 2006 eng d00aClustering Similarity Comparison Using Density Profiles and its Application to the Discovery of Alternate Clusterings.0 aClustering Similarity Comparison Using Density Profiles and its 1 aDong, Guozhu1 aBailey, James1 aBae, Eric, Kyoo Han uhttp://knoesis.wright.edu/node/107500451nas a2200145 4500008004100000245006200041210006200103100001700165700001900182700001800201700001600219700001600235700001400251856004000265 2006 eng d00aegression Cubes with Lossless Compression and Aggregation0 aegression Cubes with Lossless Compression and Aggregation1 aDong, Guozhu1 aWang, Jianyong1 aWah, Benjamin1 aChen, Yixin1 aHan, Jiawei1 aPei, Jian uhttp://knoesis.wright.edu/node/150100487nas a2200109 4500008004100000245010900041210006900150260008500219100001600304700001700320856004000337 2006 eng d00aMasquerader Detection Using OCLEP: One-Class Classification Using Length Statistics of Emerging Patterns0 aMasquerader Detection Using OCLEP OneClass Classification Using bInternational Workshop on INformation Processing over Evolving Networks (WINPEN)1 aChen, Lijun1 aDong, Guozhu uhttp://knoesis.wright.edu/node/181400452nas a2200133 4500008004100000245009300041210006900134100001800203700001500221700001700236700001100253700001400264856004000278 2006 eng d00aMinimum Description Length (MDL) Principle: Generators Are Preferable to Closed Patterns0 aMinimum Description Length MDL Principle Generators Are Preferab1 aWong, Limsoon1 aLi, Jinyan1 aDong, Guozhu1 aLi, H.1 aPei, Jian uhttp://knoesis.wright.edu/node/110500357nas a2200097 4500008004100000245007600041210006900117100001600186700001700202856004000219 2006 eng d00aSuccinct and Informative Cluster Descriptions for Document Repositories0 aSuccinct and Informative Cluster Descriptions for Document Repos1 aChen, Lijun1 aDong, Guozhu uhttp://knoesis.wright.edu/node/110400507nas a2200157 4500008004100000245008600041210006900127100001600196700001400212700001600226700001300242700001700255700001800272700001900290856004000309 2004 eng d00aCai: Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams.0 aCai Stream Cube An Architecture for MultiDimensional Analysis of1 aChen, Yixin1 aPei, Jian1 aHan, Jiawei1 aDora, Y.1 aDong, Guozhu1 aWah, Benjamin1 aWang, Jianyong uhttp://knoesis.wright.edu/node/149900396nas a2200109 4500008004100000245007800041210006900119100001800188700002300206700001700229856004000246 2004 eng d00aOn the decidability of the termination problem of active database systems0 adecidability of the termination problem of active database syste1 aBailey, James1 aRammamohanarao, K.1 aDong, Guozhu uhttp://knoesis.wright.edu/node/150000415nas a2200121 4500008004100000245006800041210006600109100001800175700002800193700001500221700001700236856004000253 2004 eng d00aDeEPs: A New Instance-based Discovery and Classification System0 aDeEPs A New Instancebased Discovery and Classification System1 aWong, Limsoon1 aRamamohanarao, Kotagiri1 aLi, Jinyan1 aDong, Guozhu uhttp://knoesis.wright.edu/node/149700343nas a2200109 4500008004100000245004900041210004900090100001900139700001800158700001700176856004000193 2003 eng d00aIncremental Recomputation in Local Languages0 aIncremental Recomputation in Local Languages1 aLibkin, Leonid1 aWong, Limsoon1 aDong, Guozhu uhttp://knoesis.wright.edu/node/148100492nas a2200145 4500008004100000245009000041210006900131100001500200700001700215700001600232700001700248700001400265700002700279856004000306 2003 eng d00aOnline Mining of Changes from Data Streams: Research Problems and Preliminary Results0 aOnline Mining of Changes from Data Streams Research Problems and1 aYu, Philip1 aDong, Guozhu1 aHan, Jiawei1 aWang, Haixun1 aPei, Jian1 aLakshmanan, Laks, V.S. uhttp://knoesis.wright.edu/node/149600396nam a2200109 4500008004100000245008600041210006900127100001400196700001700210700001900227856004000246 2003 eng d00aProceedings of the 4th International Conference on Web-Age Information Management0 aProceedings of the 4th International Conference on WebAge Inform1 aWang, Wei1 aDong, Guozhu1 aTang, Changjie uhttp://knoesis.wright.edu/node/201200399nas a2200109 4500008004100000245008900041210006900130100001700199700001900216700001400235856004000249 2003 eng d00aProceedings of The Fourth International Conference on Web-Age Information Management0 aProceedings of The Fourth International Conference on WebAge Inf1 aDong, Guozhu1 aTang, Changjie1 aWang, Wei uhttp://knoesis.wright.edu/node/149800417nas a2200133 4500008004100000245006000041210005800101100001600159700001800175700001700193700001300210700002000223856004000243 2003 eng d00aPushing Aggregate Constraints by Divide-and-Approximate0 aPushing Aggregate Constraints by DivideandApproximate1 aHan, Jiawei1 aJiang, Yudong1 aDong, Guozhu1 aWang, Ke1 aYu, Jeffrey, Xu uhttp://knoesis.wright.edu/node/150300360nas a2200121 4500008004100000245005000041210004700091100001300138700001600151700001400167700001700181856004000198 2002 eng d00aOn Computing Condensed Frequent Pattern Bases0 aComputing Condensed Frequent Pattern Bases1 aZou, Wei1 aHan, Jiawei1 aPei, Jian1 aDong, Guozhu uhttp://knoesis.wright.edu/node/147600328nas a2200133 4500008004100000245001700041210001700058100001700075700001900092700001300111700001600124700001400140856004000154 2002 eng d00aCubeExplorer0 aCubeExplorer1 aDong, Guozhu1 aWang, Jianyong1 aWang, Ke1 aHan, Jiawei1 aPei, Jian uhttp://knoesis.wright.edu/node/154400462nas a2200145 4500008004100000245007000041210006800111100001400179700001900193700001800212700001600230700001300246700001700259856004000276 2002 eng d00aMultiDimensional Regression Analysis of Time-Series Data Streams.0 aMultiDimensional Regression Analysis of TimeSeries Data Streams1 aPei, Jian1 aWang, Jianyong1 aWah, Benjamin1 aHan, Jiawei1 aZou, Wei1 aDong, Guozhu uhttp://knoesis.wright.edu/node/111300439nas a2200133 4500008004100000245007000041210006800111100001900179700001600198700001700214700001800231700001600249856004000265 2002 eng d00aMulti-Dimensional Regression Analysis of Time-Series Data Streams0 aMultiDimensional Regression Analysis of TimeSeries Data Streams1 aWang, Jianyong1 aHan, Jiawei1 aDong, Guozhu1 aWah, Benjamin1 aChen, Yixin uhttp://knoesis.wright.edu/node/107800449nas a2200145 4500008004100000245006200041210006000103100001400163700001800177700001700195700001600212700001900228700001600247856004000263 2002 eng d00aOnline analytical processing stream data: is it feasible?0 aOnline analytical processing stream data is it feasible1 aPei, Jian1 aWah, Benjamin1 aDong, Guozhu1 aChen, Yixin1 aWang, Jianyong1 aHan, Jiawei uhttp://knoesis.wright.edu/node/154500394nas a2200109 4500008004100000245007000041210006900111100002800180700001700208700001900225856004000244 2001 eng d00aBuilding behavior knowledge space to make classification decision0 aBuilding behavior knowledge space to make classification decisio1 aRamamohanarao, Kotagiri1 aDong, Guozhu1 aZhang, Xiuzhen uhttp://knoesis.wright.edu/node/155300400nas a2200109 4500008004100000245008000041210006900121100001500190700001700205700002800222856004000250 2001 eng d00aCombining the strength of pattern frequency and distance for classification0 aCombining the strength of pattern frequency and distance for cla1 aLi, Jinyan1 aDong, Guozhu1 aRamamohanarao, Kotagiri uhttp://knoesis.wright.edu/node/155200391nas a2200121 4500008004100000245006400041210006400105100001400169700001600183700001300199700001700212856004000229 2001 eng d00aEfficient Computation of Iceberg Cubes with Complex Measure0 aEfficient Computation of Iceberg Cubes with Complex Measure1 aPei, Jian1 aHan, Jiawei1 aWang, Ke1 aDong, Guozhu uhttp://knoesis.wright.edu/node/155000313nas a2200097 4500008004100000245004800041210004800089100002100137700001700158856004000175 2001 eng d00aEfficient Mining of Niches and Set Routines0 aEfficient Mining of Niches and Set Routines1 aDeshpe, Kaustubh1 aDong, Guozhu uhttp://knoesis.wright.edu/node/154900258nas a2200085 4500008004100000245003700041210003700078100001700115856004000132 2001 eng d00aKnowledge discovery in databases0 aKnowledge discovery in databases1 aDong, Guozhu uhttp://knoesis.wright.edu/node/206000403nas a2200109 4500008004100000245008300041210006900124100002800193700001700221700001500238856004000253 2001 eng d00aMaking Use of the Most Expressive Jumping Emerging Patterns for Classification0 aMaking Use of the Most Expressive Jumping Emerging Patterns for 1 aRamamohanarao, Kotagiri1 aDong, Guozhu1 aLi, Jinyan uhttp://knoesis.wright.edu/node/154600420nas a2200133 4500008004100000245006600041210006400107100001500171700001300186700001400199700001700213700001600230856004000246 2001 eng d00aMining Multi-Dimensional Constrained Gradients in Data Cubes.0 aMining MultiDimensional Constrained Gradients in Data Cubes1 aLam, Joyce1 aWang, Ke1 aPei, Jian1 aDong, Guozhu1 aHan, Jiawei uhttp://knoesis.wright.edu/node/154800403nas a2200133 4500008004100000245005200041210005200093100001500145700001900160700001800179700001700197700001500214856004000229 2001 eng d00aQuery processing with an FPGA Coprocessor Board0 aQuery processing with an FPGA Coprocessor Board1 aJean, Jack1 aZhang, Baifeng1 aGuo, Xinzhong1 aDong, Guozhu1 aZhang, Hwa uhttp://knoesis.wright.edu/node/154700333nas a2200109 4500008004100000245004100041210004100082100002800123700001500151700001700166856004000183 2000 eng d00aEmerging Patterns and Classification0 aEmerging Patterns and Classification1 aRamamohanarao, Kotagiri1 aLi, Jinyan1 aDong, Guozhu uhttp://knoesis.wright.edu/node/153900425nas a2200109 4500008004100000245010100041210006900142100001700211700002800228700001900256856004000275 2000 eng d00aExploring Constraints to Efficiently Mine Emerging Patterns from Large High-dimensional Datasets0 aExploring Constraints to Efficiently Mine Emerging Patterns from1 aDong, Guozhu1 aRamamohanarao, Kotagiri1 aZhang, Xiuzhen uhttp://knoesis.wright.edu/node/113300358nas a2200097 4500008004100000245007700041210006900118100001700187700001600204856004000220 2000 eng d00aIncremental Maintenance of Recursive Views Using Relational Calculus/SQL0 aIncremental Maintenance of Recursive Views Using Relational Calc1 aDong, Guozhu1 aSu, Jianwen uhttp://knoesis.wright.edu/node/154000394nas a2200109 4500008004100000245007000041210006900111100001900180700001700199700002800216856004000244 2000 eng d00aInformation-based Classification by Aggregating Emerging Patterns0 aInformationbased Classification by Aggregating Emerging Patterns1 aZhang, Xiuzhen1 aDong, Guozhu1 aRamamohanarao, Kotagiri uhttp://knoesis.wright.edu/node/113500360nas a2200109 4500008004100000245005500041210005400096100001700150700001500167700002800182856004000210 2000 eng d00aInstance-based classification by emerging patterns0 aInstancebased classification by emerging patterns1 aDong, Guozhu1 aLi, Jinyan1 aRamamohanarao, Kotagiri uhttp://knoesis.wright.edu/node/235600325nas a2200109 4500008004100000245004000041210004000081100001700121700001800138700001900156856004000175 2000 eng d00aLocal Properties of Query Languages0 aLocal Properties of Query Languages1 aDong, Guozhu1 aWong, Limsoon1 aLibkin, Leonid uhttp://knoesis.wright.edu/node/154200403nas a2200109 4500008004100000245008300041210006900124100001700193700001500210700002800225856004000253 2000 eng d00aMaking Use of the Most Expressive Jumping Emerging Patterns for Classification0 aMaking Use of the Most Expressive Jumping Emerging Patterns for 1 aDong, Guozhu1 aLi, Jinyan1 aRamamohanarao, Kotagiri uhttp://knoesis.wright.edu/node/154100358nas a2200109 4500008004100000245005400041210005300095100002800148700001700176700001500193856004000208 2000 eng d00anstance-based classification by emerging patterns0 anstancebased classification by emerging patterns1 aRamamohanarao, Kotagiri1 aDong, Guozhu1 aLi, Jinyan uhttp://knoesis.wright.edu/node/113400457nas a2200145 4500008004100000245006200041210006200103100001800165700001800183700001700201700002200218700001500240700001600255856004000271 2000 eng d00aOptimization techniques for data intensive decision flows0 aOptimization techniques for data intensive decision flows1 aHull, Richard1 aKumar, Bharat1 aDong, Guozhu1 aLlirbat, Francois1 aZhou, Gang1 aSu, Jianwen uhttp://knoesis.wright.edu/node/112900394nas a2200097 4500008004100000245011100041210006900152100001700221700001800238856004000256 2000 eng d00aSeparating Auxiliary Arity Hierarchy of First-Order Incremental Evaluation Using (3+1)-ary Input Relations0 aSeparating Auxiliary Arity Hierarchy of FirstOrder Incremental E1 aDong, Guozhu1 aZhang, Louxin uhttp://knoesis.wright.edu/node/153800395nas a2200109 4500008004100000245007500041210006900116100001500185700002800200700001700228856004000245 2000 eng d00aThe Space of Jumping Emerging Patterns and Its Incremental Maintenance0 aSpace of Jumping Emerging Patterns and Its Incremental Maintenan1 aLi, Jinyan1 aRamamohanarao, Kotagiri1 aDong, Guozhu uhttp://knoesis.wright.edu/node/154300387nas a2200121 4500008004100000245005800041210005700099100001800156700001700174700001900191700001500210856004000225 1999 eng d00aCAEP: Classification by Aggregating Emerging Patterns0 aCAEP Classification by Aggregating Emerging Patterns1 aWong, Limsoon1 aDong, Guozhu1 aZhang, Xiuzhen1 aLi, Jinyan uhttp://knoesis.wright.edu/node/153200406nas a2200121 4500008004100000245006900041210006900110100001600179700001700195700001700212700001500229856004000244 1999 eng d00aData Integration by Describing Sources with Constraint Databases0 aData Integration by Describing Sources with Constraint Databases1 aSu, Jianwen1 aDong, Guozhu1 aLau, Tzekwan1 aCheng, Xun uhttp://knoesis.wright.edu/node/112000328nas a2200097 4500008004100000245005700041210005700098100001700155700001800172856004000190 1999 eng d00aDecidability of First Order Logic Queries over Views0 aDecidability of First Order Logic Queries over Views1 aDong, Guozhu1 aBailey, James uhttp://knoesis.wright.edu/node/111800508nas a2200157 4500008004100000245007800041210006900119100001700188700001800205700002200223700001500245700001600260700001600276700001800292856004000310 1999 eng d00aDeclarative Workflows that Support Easy Modification and Dynamic Browsing0 aDeclarative Workflows that Support Easy Modification and Dynamic1 aDong, Guozhu1 aHull, Richard1 aLlirbat, Francois1 aZhou, Gang1 aSu, Jianwen1 aSimon, Eric1 aKumar, Bharat uhttp://knoesis.wright.edu/node/112100386nas a2200109 4500008004100000245007500041210006900116100001900185700001500204700001700219856004000236 1999 eng d00aDiscovering Jumping Emerging Patterns and Experiments on Real Datasets0 aDiscovering Jumping Emerging Patterns and Experiments on Real Da1 aZhang, Xiuzhen1 aLi, Jinyan1 aDong, Guozhu uhttp://knoesis.wright.edu/node/153600358nas a2200097 4500008004100000245007800041210006900119100001500188700001700203856004000220 1999 eng d00aEfficient Mining of Emerging Patterns: Discovering Trends and Differences0 aEfficient Mining of Emerging Patterns Discovering Trends and Dif1 aLi, Jinyan1 aDong, Guozhu uhttp://knoesis.wright.edu/node/113200461nas a2200133 4500008004100000245008500041210006900126100001700195700001500212700001300227700002800240700001900268856004000287 1999 eng d00aEfficient Mining of High Confidence Association Rules without Support Thresholds0 aEfficient Mining of High Confidence Association Rules without Su1 aDong, Guozhu1 aLi, Jinyan1 aSun, Qun1 aKotagiri, Ramamohanarao1 aZhang, Xiuzhen uhttp://knoesis.wright.edu/node/113100382nas a2200109 4500008004100000245007400041210006900115100001600184700001700200700001500217856004000232 1999 eng d00aEfficient Mining of Partial Periodic Patterns in Time Series Database0 aEfficient Mining of Partial Periodic Patterns in Time Series Dat1 aHan, Jiawei1 aDong, Guozhu1 aYin, Yiwen uhttp://knoesis.wright.edu/node/112200468nas a2200145 4500008004100000245006800041210006700109100001500176700001600191700001800207700001800225700001700243700002200260856004000282 1999 eng d00aEfficient support for decision flows in e-commerce applications0 aEfficient support for decision flows in ecommerce applications1 aZhou, Gang1 aSu, Jianwen1 aHull, Richard1 aKumar, Bharat1 aDong, Guozhu1 aLlirbat, Francois uhttp://knoesis.wright.edu/node/113000423nas a2200133 4500008004100000245006300041210006100104100001500165700001800180700001700198700001800215700001600233856004000249 1999 eng d00aA Framework for Optimising Distributed Workflow Executions0 aFramework for Optimising Distributed Workflow Executions1 aZhou, Gang1 aKumar, Bharat1 aDong, Guozhu1 aHull, Richard1 aSu, Jianwen uhttp://knoesis.wright.edu/node/153500306nas a2200097 4500008004100000245004600041210004600087100001700133700001800150856004000168 1999 eng d00aIncremental Evaluation of Datalog Queries0 aIncremental Evaluation of Datalog Queries1 aDong, Guozhu1 aTopor, Rodney uhttp://knoesis.wright.edu/node/151800435nas a2200109 4500008004100000245011700041210006900158100002800227700001300255700001700268856004000285 1999 eng d00aIncremental FO(+,<) Maintenance of All-pairs Shortest Paths for Undirected Graphs After Insertions and Deletions0 aIncremental FO Maintenance of Allpairs Shortest Paths for Undire1 aKotagiri, Ramamohanarao1 aPang, C.1 aDong, Guozhu uhttp://knoesis.wright.edu/node/111900297nas a2200085 4500008004100000245005700041210005600098100001700154856004000171 1999 eng d00aIncremental Maintenance of Recursive Views: A Survey0 aIncremental Maintenance of Recursive Views A Survey1 aDong, Guozhu uhttp://knoesis.wright.edu/node/153700377nas a2200121 4500008004100000245005200041210005200093100001900145700001600164700001700180700001800197856004000215 1999 eng d00aMaintaining Transitive Closure of Graphs in SQL0 aMaintaining Transitive Closure of Graphs in SQL1 aLibkin, Leonid1 aSu, Jianwen1 aDong, Guozhu1 aWong, Limsoon uhttp://knoesis.wright.edu/node/153400396nas a2200109 4500008004100000245008200041210006900123100001900192700001800211700001700229856004000246 1999 eng d00aUsing CAEP to Predict Translation Initiation Sites from Genomic DNA Sequences0 aUsing CAEP to Predict Translation Initiation Sites from Genomic 1 aZhang, Xiuzhen1 aWong, Limsoon1 aDong, Guozhu uhttp://knoesis.wright.edu/node/153300390nas a2200097 4500008004100000245010100041210006900142100002400211700001700235856004000252 1998 eng d00aBounds for First-Order Incremental Evaluation and Definition of Polynomial Time Database Queries0 aBounds for FirstOrder Incremental Evaluation and Definition of P1 aArity, Jianwen, Su.1 aDong, Guozhu uhttp://knoesis.wright.edu/node/151600420nas a2200109 4500008004100000245009700041210006900138100001800207700002800225700001700253856004000270 1998 eng d00aDecidability and Undecidability Results for the Termination Problem of Active Database Rules0 aDecidability and Undecidability Results for the Termination Prob1 aBailey, James1 aKotagiri, Ramamohanarao1 aDong, Guozhu uhttp://knoesis.wright.edu/node/151500415nas a2200121 4500008004100000245007900041210006900120100001700189700001600206700001300222700001800235856004000253 1998 eng d00aEfficient Incremental View Maintenance in Distributed Databases by Tagging0 aEfficient Incremental View Maintenance in Distributed Databases 1 aDong, Guozhu1 aMohania, M.1 aWang, X.1 aBailey, James uhttp://knoesis.wright.edu/node/151400378nas a2200097 4500008004100000245009800041210006900139100001500208700001700223856004000240 1998 eng d00aInterestingness of Discovered Association Rules in terms of Neighborhood-Based Unexpectedness0 aInterestingness of Discovered Association Rules in terms of Neig1 aLi, Jinyan1 aDong, Guozhu uhttp://knoesis.wright.edu/node/151900403nas a2200121 4500008004100000245006200041210006200103100002200165700001900187700001700206700001800223856004000241 1998 eng d00aRelational expressive power of constraint query languages0 aRelational expressive power of constraint query languages1 aBenedikt, Michael1 aLibkin, Leonid1 aDong, Guozhu1 aWong, Limsoon uhttp://knoesis.wright.edu/node/151700300nas a2200097 4500008004100000245004400041210004400085100001600129700001700145856004000162 1997 eng d00aDeterministic FOIES are Strictly Weaker0 aDeterministic FOIES are Strictly Weaker1 aSu, Jianwen1 aDong, Guozhu uhttp://knoesis.wright.edu/node/150700366nas a2200097 4500008004100000245008800041210006900129100001700198700001300215856004000228 1997 eng d00aFirst-order maintenance of transitive closure after node-set and edge-set deletions0 aFirstorder maintenance of transitive closure after nodeset and e1 aDong, Guozhu1 aPang, C. uhttp://knoesis.wright.edu/node/151000325nas a2200109 4500008004100000245004000041210004000081100001800121700001900139700001700158856004000175 1997 eng d00aLocal properties of query languages0 aLocal properties of query languages1 aWong, Limsoon1 aLibkin, Leonid1 aDong, Guozhu uhttp://knoesis.wright.edu/node/151100363nas a2200097 4500008004100000245007000041210006900111100002800180700001700208856004000225 1997 eng d00aMaintaining constrained transitive closure by conjunctive queries0 aMaintaining constrained transitive closure by conjunctive querie1 aKotagiri, Ramamohanarao1 aDong, Guozhu uhttp://knoesis.wright.edu/node/150900352nas a2200097 4500008004100000245006900041210006900110100001700179700001800196856004000214 1997 eng d00aSome Relationships between FOIES and Sigma 1 1 Arity Hierarchies0 aSome Relationships between FOIES and Sigma 1 1 Arity Hierarchies1 aDong, Guozhu1 aWong, Limsoon uhttp://knoesis.wright.edu/node/150600344nas a2200109 4500008004100000245004500041210004500086100002800131700001800159700001700177856004000194 1997 eng d00aStructural issues in active rule systems0 aStructural issues in active rule systems1 aRamamohanarao, Kotagiri1 aBailey, James1 aDong, Guozhu uhttp://knoesis.wright.edu/node/150500344nas a2200097 4500008004100000245006600041210006600107100001600173700001700189856004000206 1996 eng d00aAlgorithms for adapting materialised views in data warehouses0 aAlgorithms for adapting materialised views in data warehouses1 aMohania, M.1 aDong, Guozhu uhttp://knoesis.wright.edu/node/151300352nas a2200097 4500008004100000245007100041210006900112100001700181700001600198856004000214 1996 eng d00aConjunctive query containment with repect to views and constraints0 aConjunctive query containment with repect to views and constrain1 aDong, Guozhu1 aSu, Jianwen uhttp://knoesis.wright.edu/node/150401862nas a2200145 4500008004100000245006200041210006200103300000900165520142600174100002201600700001701622700001901639700001801658856004001676 1996 eng d00aRelational Expressive Power of Constraint Query Languages0 aRelational Expressive Power of Constraint Query Languages a5-163 aThe expressive power of first-order query languages with several classes of equality and inequality constraints is studied in this paper. We settle the conjecture that recursive queries such as parity test and transitive closure cannot be expressed in the relational calculus augmented with polynomial inequality constraints over the reals. Furthermore, noting that relational queries exhibit several forms of genericity, we establish a number of collapse results of the following form: The class of generic boolean queries expressible in the relational calculus augmented with a given class of constraints coincides with the class of queries expressible in the relational calculus (with or without an order relation). We prove such results for both the natural and active-domain semantics. As a consequence, the relational calculus augmented with polynomial inequalities expresses the same classes of generic boolean queries under both the natural and active-domain semantics. In the course of proving these results for the active-domain semantics, we establish Ramsey-type theorems saying that any query involving certain kinds of constraints coincides with a constraint free query on databases whose elements come from a certain innite subset of the domain. To prove the collapse results for the natural semantics, we make use of techniques from nonstandard analysis and from the model theory of ordered structures.
1 aBenedikt, Michael1 aDong, Guozhu1 aLibkin, Leonid1 aWong, Limsoon uhttp://knoesis.wright.edu/node/150800374nas a2200097 4500008004100000245008700041210006900128100002200197700001700219856004000236 1995 eng d00aOn decompositions of chain datalog programs into P (left)-linear 1-rule components0 adecompositions of chain datalog programs into P leftlinear 1rule1 aGinsburg, Seymour1 aDong, Guozhu uhttp://knoesis.wright.edu/node/152800491nas a2200121 4500008004100000245010100041210006900142260006400211100001700275700001900292700001800311856004000329 1995 eng d00aOn impossibility of decremental recomputation of recursive queries in relational calculus and SQ0 aimpossibility of decremental recomputation of recursive queries bFifth International Database Programming Languages Workshop1 aDong, Guozhu1 aLibkin, Leonid1 aWong, Limsoon uhttp://knoesis.wright.edu/node/181700369nas a2200097 4500008004100000245008800041210006900129100001700198700001600215856004000231 1995 eng d00aIncremental and Decremental Evaluation of Transitive Closure by First-Order Queries0 aIncremental and Decremental Evaluation of Transitive Closure by 1 aDong, Guozhu1 aSu, Jianwen uhttp://knoesis.wright.edu/node/151200388nas a2200097 4500008004100000245010700041210006900148100001600217700001700233856004000250 1995 eng d00aIncremental boundedness and nonrecursive incremental evaluation of datalog queries (extended abstract)0 aIncremental boundedness and nonrecursive incremental evaluation 1 aSu, Jianwen1 aDong, Guozhu uhttp://knoesis.wright.edu/node/112800291nas a2200085 4500008004100000245005700041210005000098100001700148856004000165 1995 eng d00aOn the index of positive programmed formal languages0 aindex of positive programmed formal languages1 aDong, Guozhu uhttp://knoesis.wright.edu/node/152900360nas a2200109 4500008004100000245005900041210005900100100001700159700001600176700001800192856004000210 1995 eng d00aNonrecursive Incremental Evaluation of Datalog Queries0 aNonrecursive Incremental Evaluation of Datalog Queries1 aDong, Guozhu1 aSu, Jianwen1 aTopor, Rodney uhttp://knoesis.wright.edu/node/152500260nas a2200097 4500008004100000245002400041210002400065100001700089700001600106856004000122 1995 eng d00aSpace bounded FOIES0 aSpace bounded FOIES1 aDong, Guozhu1 aSu, Jianwen uhttp://knoesis.wright.edu/node/112700338nas a2200097 4500008004100000245006600041210006300107100001300170700001700183856004000200 1994 eng d00aA framework for object migration in object-oriented databases0 aframework for object migration in objectoriented databases1 aLi, Qing1 aDong, Guozhu uhttp://knoesis.wright.edu/node/152600353nam a2200109 4500008004100000245005900041210005800100100001400158700001400172700001700186856004000203 1993 eng d00aDiscussion Report: Object Migration and Classification0 aDiscussion Report Object Migration and Classification1 aDavis, K.1 aHeuer, A.1 aDong, Guozhu uhttp://knoesis.wright.edu/node/201500327nas a2200097 4500008004100000245005800041210005700099100001600156700001700172856004000189 1993 eng d00aFirst-order incremental evaluation of datalog queries0 aFirstorder incremental evaluation of datalog queries1 aSu, Jianwen1 aDong, Guozhu uhttp://knoesis.wright.edu/node/152700291nas a2200085 4500008004100000245005800041210004900099100001700148856004000165 1993 eng d00aOn the monotonicity of (LDL) logic programs with sets0 amonotonicity of LDL logic programs with sets1 aDong, Guozhu uhttp://knoesis.wright.edu/node/153100334nas a2200085 4500008004100000245008100041210006900122100001700191856004000208 1992 eng d00aDatalog expressiveness of chain queries: Grammar tools and characterisations0 aDatalog expressiveness of chain queries Grammar tools and charac1 aDong, Guozhu uhttp://knoesis.wright.edu/node/152200306nas a2200097 4500008004100000245004600041210004600087100001800133700001700151856004000168 1992 eng d00aIncremental evaluation of datalog queries0 aIncremental evaluation of datalog queries1 aTopor, Rodney1 aDong, Guozhu uhttp://knoesis.wright.edu/node/153000308nas a2200097 4500008004100000245005000041210004900091100001300140700001700153856004000170 1992 eng d00aObject migration in object-oriented databases0 aObject migration in objectoriented databases1 aLi, Qing1 aDong, Guozhu uhttp://knoesis.wright.edu/node/152000393nas a2200097 4500008004100000245010000041210006900141100001700210700002800227856004000255 1992 eng d00aRepresentation and translation of queries in heterogeneous databases with semantic discrepancie0 aRepresentation and translation of queries in heterogeneous datab1 aDong, Guozhu1 aRamamohanarao, Kotagiri uhttp://knoesis.wright.edu/node/152100273nas a2200085 4500008004100000245004600041210004300087100001700130856004000147 1991 eng d00aOn Datalog Linearisation of Chain Queries0 aDatalog Linearisation of Chain Queries1 aDong, Guozhu uhttp://knoesis.wright.edu/node/152300316nas a2200097 4500008004100000245004900041210004900090100001700139700002200156856004000178 1991 eng d00aLocalizable constraints for object histories0 aLocalizable constraints for object histories1 aDong, Guozhu1 aGinsburg, Seymour uhttp://knoesis.wright.edu/node/149200356nas a2200109 4500008004100000245003300041210003300074260006600107100001600173700001700189856004000206 1991 eng d00aObject behaviors and scripts0 aObject behaviors and scripts b3rd Internat'l Workshop on Database Programming Languages1 aSu, Jianwen1 aDong, Guozhu uhttp://knoesis.wright.edu/node/180500317nas a2200097 4500008004100000245005300041210004600094100002200140700001700162856004000179 1990 eng d00aOn the decomposition of datalog program mappings0 adecomposition of datalog program mappings1 aGinsburg, Seymour1 aDong, Guozhu uhttp://knoesis.wright.edu/node/149100417nas a2200097 4500008004100000245010300041210006900144260004900213100001700262856004000279 1990 eng d00aInference of cyclicity and acyclicity constraints among recursively typed objects with identifiers0 aInference of cyclicity and acyclicity constraints among recursiv bFar-East Workshop on Future Database Systems1 aDong, Guozhu uhttp://knoesis.wright.edu/node/180400331nas a2200085 4500008004100000245007800041210006900119100001700188856004000205 1989 eng d00aOn distributed processibility of datalog queries by decomposing databases0 adistributed processibility of datalog queries by decomposing dat1 aDong, Guozhu uhttp://knoesis.wright.edu/node/109700315nas a2200085 4500008004100000245006900041210006200110100001700172856004000189 1988 eng d00aOn the composition and decomposition of datalog program mappings0 acomposition and decomposition of datalog program mappings1 aDong, Guozhu uhttp://knoesis.wright.edu/node/148200316nas a2200097 4500008004100000245004900041210004900090100001700139700002200156856004000178 1986 eng d00aLocalizable Constraints for Object Histories0 aLocalizable Constraints for Object Histories1 aDong, Guozhu1 aGinsburg, Seymour uhttp://knoesis.wright.edu/node/1733}