Search

Researchers from the same lab often spend a considerable amount of time searching for published articles relevant to their current project. Despite having similar interests, they conduct independent, time consuming searches. While they may share the results afterwards, they are unable to leverage previous search results during the search process. We propose a research paper recommender system that avoids such time consuming searches by augmenting existing search engines with recommendations based on previous searches performed by others in the lab. Most existing recommender systems were developed for commercial domains with millions of users. The research paper domain has relatively few users compared to the large number of online research papers. The two major challenges with this type of data are the large number of dimensions and the sparseness of the data. The novel contribution of the paper is a scalable subspace clustering algorithm (SCuBASCuBA: Subspace Clustering Based Analysis.) that tackles these problems. Both synthetic and benchmark datasets are used to evaluate the clustering algorithm and to demonstrate that it performs better than the traditional collaborative filtering approaches when recommending research papers. [ABSTRACT FROM AUTHOR]

This paper presents the advantage that knowledge-intensive activities, such as Scientific Conference Management, can take by the exploitation of expert components in the key tasks. Typically, in this domain the task of scheduling the activities and resources or the assignment of reviewers to papers is performed manually leading therefore to time-consuming procedures with high degree of inconsistency due to many parameters and constraints to be considered. The proposed systems, evaluated on real conference datasets, show good results compared to manual scheduling and assignment, in terms of both accuracy and reduction of runtime. [ABSTRACT FROM AUTHOR]

Vertical Search Engines (VSEs), which usually work on specific domains, are designed to answer complex queries of professional users. VSEs usually have large repositories of structured instances. Traditional instance ranking methods do not consider the categories that instances belong to. However, users of different interests usually care only the ranking list in their own communities. In this paper we design a ranking algorithm -ZRank, to rank the classified instances according to their importances in specific categories. To test our idea, we develop a scientific paper search engine-CPaper. By employing instance classifying and ranking algorithms, we discover some helpful facts to users of different interests. [ABSTRACT FROM AUTHOR]

This paper presents the integration of linguistic knowledge in learning semantic user profiles able to represent user interests in a more effective way with respect to classical keyword-based profilesThis research was partially funded by the EC under the 6th FP IST IP VIKEF No. 507173, Priority 2.3.1.7 Semantic Based Knowledge Systems (www.vikef.net) and under the DELOS NoE on Digital Libraries, Priority: Technology-enhanced Learning and Access to Cultural Heritage - Contract n. G038-507618 (www.delos.info). The work was supported also by the research project "Modelli avanzati di personalizzazione per biblioteche elettroniche", year 2005, funded by the University of Bari.. Semantic profiles are obtained by integrating a naïve Bayes approach for text categorization with a word sense disambiguation strategy based on the WordNet lexical database (Section 2). Semantic profiles are exploited by the "conference participant advisor" service in order to suggest papers to be read and talks to be attended by a conference participant. Experiments on a real dataset show the effectiveness of the service (Section 3). [ABSTRACT FROM AUTHOR]

In Service-Oriented Architectures (SOAs), software systems are decomposed into independent units, namely services, that interact with one another through message exchanges. To promote reuse and evolvability, these interactions are explicitly described right from the early phases of the development lifecycle. Up to now, emphasis has been placed on capturing structural aspects of service interactions. Gradually though, the description of behavioral dependencies between service interactions is gaining increasing attention as a means to push forward the SOA vision. This paper deals with the description of these behavioral dependencies during the analysis and design phases. The paper outlines a set of requirements that a language for modeling service interactions at this level should fulfill, and proposes a language whose design is driven by these requirements. [ABSTRACT FROM AUTHOR]

Recognizing that trust states are mental states, this paper presents a formal analysis of the dynamics of trust in terms of the functional roles and representation relations for trust states. This formal analysis is done both in a logical framework and in a mathematical framework based on integral and differential equations. Furthermore, the paper presents formal specifications of a number of relevant dynamic properties of trust. The specifications provided were used to perform automated formal analysis of empirical and simulated data from two case studies, one involving two experiments with humans, and one involving simulation experiments in the context of an economic game. [ABSTRACT FROM AUTHOR]

Using XML views to answer the XML query is an important query optimization strategy especially in the distributed environment. Although many methods have been proposed to handle the single XML view rewriting, they will lead to the redundant computation cost due to the shared paths among different XML views. This paper handles the query rewriting over multiple views by organizing the multiple XML views into a tree called MQTree, in which the shared sub paths among the multiple views have been merged in a top down fashion. In addition, this paper designs a MQTree based query rewriting method. The candidate query rewriting plans are generated over MQTree directly. In order to reduce the validation cost of the candidate query rewriting plans, the preliminary validation is made at the granularity of the path query {//,/,*} over the MQTree first, which prunes the candidate views further and provides the intermediate results for the plans validation at the granularity of the whole tree. The final experiments show the efficiency and effectiveness of our method. [ABSTRACT FROM AUTHOR]

In this paper the problem of matching a query with imprecise or missing data is analyzed for geographic information systems, and an approach for the relaxation query constraints is proposed. This approach, similarly with the 9-intersection matrix between two sets of points presented in [1] [2] [3], proposes the representation of two symbolic graphical objects (SGO) in terms of interior, boundary, and exterior points, applied to each of the configurations between two objects. The paper distinguishes the different configurations considering the results obtained from the 9 intersections, not only by whether results are null or not null. This approach allows a more detailed similarity graph to be defined. The aim of the proposed methodology is to relax geographical query constraints, in order to obtain meaningful answers for imprecise or missing data. In this paper the polyline-polygon case is discussed in detail. Keywords: Geographical query languages, relaxing constraints, spatial operators. [ABSTRACT FROM AUTHOR]

This paper is about how to efficiently evaluate partially-dimensional range queries, which are often used in many actual applications. If the existing multidimensional indices are employed to evaluate partially-dimensional range queries, then a great deal of information that is irrelevant to the queries also has to be read from disk. A modification of R*-tree is described in this paper to ameliorate such a situation. Discussions and experiments indicate that the proposed modification can clearly improve the performance of partially-dimensional range queries, especially for large datasets. [ABSTRACT FROM AUTHOR]

This paper presents WorkflowNet2BPEL4WS a tool to automatically map a graphical workflow model expressed in terms of Workflow Nets (WF-nets) onto BPEL. The Business Process Execution Language for Web Services (BPEL) has emerged as the de-facto standard for implementing processes and is supported by an increasing number of systems (cf. the IBM WebSphere Choreographer and the Oracle BPEL Process Manager). While being a powerful language, BPEL is difficult to use. Its XML representation is very verbose and only readable for the trained eye. It offers many constructs and typically things can be implemented in many ways, e.g., using links and the flow construct or using sequences and switches. As a result only experienced users are able to select the right construct. Some vendors offer a graphical interface that generates BPEL code. However, the graphical representations are a direct reflection of the BPEL code and not easy to use by end-users. Therefore, we provide a mapping from WF-nets to BPEL. This mapping builds on the rich theory of Petri nets and can also be used to map other languages (e.g., UML, EPC, BPMN, etc.) onto BPEL. To evaluate WorkflowNet2BPEL4WS we used more than 100 processes modeled using Protos (the most widely used business process modeling tool in the Netherlands), automatically converted these into CPN Tools, and applied our mapping. The results of these evaluation are very encouraging and show the applicability of our approach. Keywords: BPEL4WS, Petri nets, workflow management, business process management. [ABSTRACT FROM AUTHOR]

A critical challenge in keyword search over relational data- bases (KSORD) is to improve its result presentation to facilitate users' quick browsing through search results. An effective method is to organize the results into clusters. However, traditional clustering method is not applicable to KSORD search results. In this paper, we propose a novel clustering method named TreeCluster. In the first step, we use labels to represent schema information of each result tree and reformulate the clustering problem as a problem of judging whether labeled trees are isomorphic. In the second step, we rank user keywords according to their frequencies in databases, and further partition the large clusters based on keyword nodes. Furthermore, we give each cluster a readable description, and present the description and each result graphically to help users understand the results more easily. Experimental results verify our method's effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Ontologies play a key role in Semantic Web research. A common use of ontologies in Semantic Web is to enrich the current Web resources with some well-defined meaning to enhance the search capabilities of existing web searching systems. This paper reports on how ontologies developed in the EU Semantic Web project SPIRIT are used to support retrieval of documents that are considered to be spatially relevant to users' queries. The query expansion techniques presented in this paper are based on both a domain and a geographical ontology. The proposed techniques are distinguished from conventional ones in that a query is expanded by derivation of its geographical query footprint. The techniques are specially designed to resolve a query (such as castles near Edinburgh) that involves spatial terms (e.g. Edinburgh) and fuzzy spatial relationships (e.g. near) that qualify the spatial terms. Various factors are taken into account to support intelligent expansion of a spatial query, including, spatial terms as encoded in the geographical ontology, non-spatial terms as encoded in the domain ontology, as well as the semantics of the spatial relationships and their context of use. Some experiments have been carried out to evaluate the performance of the proposed techniques using sample realistic ontologies. Keywords: Ontology, Semantic Web, Spatial Search, Query Expansion. [ABSTRACT FROM AUTHOR]

Supervised learning deals with the inference of a distribution over an output or label space $\mathcal{Y}$ conditioned on points in an observation space $\mathcal{X}$, given a training dataset D of pairs in $\mathcal{X} \times \mathcal{Y}$. However, in a lot of applications of interest, acquisition of large amounts of observations is easy, while the process of generating labels is time-consuming or costly. One way to deal with this problem is active learning, where points to be labelled are selected with the aim of creating a model with better performance than that of an model trained on an equal number of randomly sampled points. In this paper, we instead propose to deal with the labelling cost directly: The learning goal is defined as the minimisation of a cost which is a function of the expected model performance and the total cost of the labels used. This allows the development of general strategies and specific algorithms for (a) optimal stopping, where the expected cost dictates whether label acquisition should continue (b) empirical evaluation, where the cost is used as a performance metric for a given combination of inference, stopping and sampling methods. Though the main focus of the paper is optimal stopping, we also aim to provide the background for further developments and discussion in the related field of active learning. [ABSTRACT FROM AUTHOR]

Coordination systems have been used in a variety of different applications but have never performed well in large scale, faulty settings. The sheer scale and level of complexity of today's applications is enough to make the current ways of thinking about distributed systems (e.g. deterministic decisions about data organization) obsolete. All the same, computer scientists are searching for new approaches and are paying more attention to stochastic approaches that provide good solutions "most of the time". The trade-off here is that by loosening certain requirements the system ends up performing better in other fronts such as adaptiveness to failures. Adaptation is a key component to fault-tolerance and tuple distribution is the center of the fault-tolerance problem in tuple-space systems. Hence, this paper shows how the tuple distribution in Linda-like systems can be solved by using an adaptive self-organized approach à la Swarm Intelligence. The results discussed in this paper demonstrate that efficient and adaptive solutions to this problem can be achieved using simple and inexpensive approaches. [ABSTRACT FROM AUTHOR]

The two dominant costs in continuous spatial query systems are the wireless communication cost for location update, and the evaluation cost for query processing. Existing works address both of these scalability factors by employing the distributed computation strategy, in which some part of query processing is carried out by mobile objects. In this paper, we make one important assumption about mobile objects' energy; that is for many applications, mobile objects' energy is not limited, as opposed to the battery-powered objects assumed in existing works. Under this new assumption, we re-examine the scalability issues for continuous spatial query systems. Our examination points out that the major bottleneck of these systems is now the wide-area wireless uplink bandwidth, which has not been addressed adequately in the past. We attack the problem by leveraging the local-area wireless communication between mobile objects, leading to our proposal of a hybrid communication architecture to be used in these continuous spatial query systems. The hybrid communication architecture unifies the two communication paradigms, wide-area and local-area wireless networks. We then propose a proof of concept system, called P2MRQ (Peer-to-peer technique for Moving Range Queries), to answer continuous moving range queries over moving objects. While MobiEyes [1], an existing continuous range query system, only utilizes distributed computation, our P2MRQ is able to leverage both distributed computation and local-area wireless communication. Our performance study shows that the required wide-area wireless uplink bandwidth from P2MRQ is consistently less than that of MobiEyes; for all considered cases, P2MRQ requires at most 50% of the wide-area wireless uplink bandwidth as MobiEyes does. [ABSTRACT FROM AUTHOR]

Because of increased stream data, managing stream data has become quite important. This paper describes our data stream management system, which employs an architecture combining a stream processing engine and DBMS. Based on the architecture, the system processes both continuous queries and traditional one-shot queries. Our proposed query language supports not only filtering, join, and projection over data streams, but also continuous persistence requirements for stream data. Users can also specify continuous queries that integrate streaming data and historical data stored in DBMS. Another contribution of this paper is feasibility validation of queries. Processing queries on streams with frequent inputs may cause the system to overflow its capacity. Specifically, the maximum writing rate to DBMS is a significant bottleneck when we try to store stream data into DBMS. Our system detects infeasible queries in advance. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of selecting right methods for materialization. In our approach, the selection of methods is based on method execution statistics that are constantly gathered in our prototype system. Based on the statistics, the prototype selects methods whose materialization is profitable. The proposed mechanism was extensively evaluated by experiments whose results are shown in this paper. [ABSTRACT FROM AUTHOR]

In this paper we describe an application based on a general approach towards modelling practical reasoning through defeasible argumentation. The purpose of the paper is to show how the incorporation of an argumentation component can add value to a collection of existing information agents. The example application is a system for reasoning about the medical treatment of a patient. An agent, called the Drama agent, orchestrates a number of information sources to supply a set of arguments on the basis of which the decision regarding treatment can be taken. We describe the general approach and its instantiation for this application, and illustrate the operation of the system with a running example. [ABSTRACT FROM AUTHOR]

It the becomes possible that users can get kinds of information by just inputting search keyword(s) representing the topic which users are interested in. But it is not always true that users can hit upon search keyword(s) properly. In this paper, by using Web access logs (called panel logs), which are collected URL histories of Japanese users (called panels) selected without static deviation similar to the survey on TV audience rating, we study the methods of clustering search keywords. Different from the existing systems where the related search keywords are extracted based on the set of URLs viewed by the users after input of their original search keyword(s), we propose two novel methods of clustering the search words. One is based on the Web communities (set of similar web pages); the other is based on the set of nouns obtained by morphological analysis of Web pages. According to evaluation results, our proposed methods can extract more related search keywords than that based on URL. [ABSTRACT FROM AUTHOR]

Nearest neighbor search has a wide variety of applications. Unfortunately, the majority of search methods do not scale well with dimensionality. Recent efforts have been focused on finding better approximate solutions that improve the locality of data using dimensionality reduction. However, it is possible to preserve the locality of data and find exact nearest neighbors in high dimensions without dimensionality reduction. This paper introduces a novel high-performance technique to find exact k-nearest neighbors in both low and high dimensional spaces. It relies on a new method for data-sensitive space partitioning based on explicit data clustering, which is introduced in the paper for the first time. This organization supports effective reduction of the search space before accessing secondary storage. Costly Euclidean distance calculations are reduced through efficient processing of a lightweight memory-based filter. The algorithm outperforms sequential scan and the VA-File in high-dimensional situations. Moreover, the results with dynamic loading of data show that the technique works well on dynamic datasets as well. [ABSTRACT FROM AUTHOR]

In recent years, many emerging database applications deal with large sets of continuously moving data objects. Since no computer system can commit continuously occurring infinitesimal changes to the database, related data management techniques view a moving object's trajectory as a sequence of discretely reported spatiotemporal points. For each pair of consecutive committed trajectory points, a spatiotemporal uncertainty region representing all possible in-between trajectory points is defined. To support trajectory queries with a non-uniform probability distribution model, the query system needs to compute (interpolate) the "most likely" trajectories in the uncertainty regions to determine the peak points of the probability distributions. This paper proposes a generalized trajectory interpolation model using parametric trajectory representations. In addition, the paper expands and investigates three practical specializations of our proposed model using a moving object with momentum, i.e., a vehicle, as the exemplar. [ABSTRACT FROM AUTHOR]

The composition of services that are indexed in a large-scale service directory often involves many complex queries issued by the service composition algorithm to the directory. These queries may cause considerable processing effort within the directory, thus limiting scalability. In this paper we present a novel approach to increase scalability: The directory offers a compact digest that service composition clients download to solve the hard part of a composition problem locally. In this paper we compare two different digest representations, a sparse matrix and a Zero-Suppressed Reduced Ordered Binary Decision Diagram (ZDD). Our evaluation confirms that both representations are compact and shows that the ZDD enables more efficient service composition.This work was supported by the Swiss National Funding Agency OFES as part of the European project KnowledgeWeb (FP6-507482). Keywords: Web services, service composition, service directories, ZDDs. [ABSTRACT FROM AUTHOR]

Several recent papers have investigated a relational approach to store XML data and there is a growing evidence that schema-conscious approaches are a better option than schema-oblivious techniques as far as query performance is concerned. This paper studies three strategies for storing XML document including one representing schema-conscious approach (Shared-Inlining) and two representing schema-oblivious approach (XParent and Sucxent++). We implement and evaluate each approach using benchmark non-recursive XQueries. Our analysis shows an interesting fact that schema-conscious approaches are not always a better option than schema-oblivious approaches! In fact, it is possible for a schema-oblivious approach (Sucxent++) to be faster than a schema-conscious approach (Shared-Inlining) for 55% of the benchmark queries (the highest observed factor being 87.8 times). Sucxent++ also outperforms XParent by up to 1700 times. [ABSTRACT FROM AUTHOR]

This paper focuses on relevance scoring for XML IR queries. We propose a novel and effective algorithm for relevance scoring, which considers both structural information and semantic. Experiments show that the algorithm can effectively improve the Precision and Recall for XML information retrieval. [ABSTRACT FROM AUTHOR]

The paper discusses the issue of hypothesis diversity in ensemble classifiers. The measures of diversity previously proposed in the literature are analyzed inside a unifying framework based on Monte Carlo stochastic algorithms. The paper shows that no measure is useful to predict ensemble performance, because all of them have only a very loose relation with the expected accuracy of the classifier. [ABSTRACT FROM AUTHOR]

A new strategy for discovering action rules (or interventions) is presented in this paper. The current methods [14], [12], [8] require to discover classification rules before any action rule can be constructed from them. Several definitions of action rules [8], [13], [9], [3] have been proposed. They differ in the generality of their classification parts but they are always constructed from certain pairs of classification rules. Our new strategy defines the classification part of an action rule in a unique way. Also, action rules are constructed from single classification rules. We show how to compute their confidence and support. Action rules are used to reclassify objects. In this paper, we propose a method for measuring the level of reclassification freedom for objects in a decision system. [ABSTRACT FROM AUTHOR]

The aim of this paper consists in investigating the problem of managing inconsistent databases, i.e. databases violating integrity constraints. A flurry of research on this topic has shown that the presence of inconsistent data can be resolved by "repairing" the database, i.e. by providing a computational mechanism that ensures obtaining consistent "scenarios" of the information or by consistently answer to queries posed on an inconsistent set of data. This paper considers preferences among repairs and possible answers by introducing a partial order among them on the basis of some preference criteria. Moreover, the paper also extends the notion of preferred consistent answer by extracting from a set of preferred repaired database, the maximal consistent overlapping portion of the information, i.e. the information supported by each preferred repaired database. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the integration of virtual knowledge communities (VKC) into an ontology-driven uncertainty model (OntoBayes). The selected overall framework for OntoBayes is the multiagent paradigm. Agents modeled with OntoBayes have two parts: knowledge and decision making parts. The former is the ontology knowledge while the latter is based upon Bayesian Networks (BN). OntoBayes is thus designed in agreement with the Agent Oriented Abstraction (AOA) paradigm. Agents modeled with OntoBayes possess a common community layer that enables to define, describe and implement corporate knowledge. This layer consists of virtual knowledge communities. [ABSTRACT FROM AUTHOR]

This paper deals with the querying of possibilistic relational databases, by means of queries called generalized yes/no queries, whose general form is: "to what extent is it possible that the answer to Q satisfies property P". Here, property P concerns the inclusion in the result, of a set of tuples specified by the user. A processing technique for such queries is proposed, which avoids computing the worlds attached to the possibilistic database. [ABSTRACT FROM AUTHOR]

In our continuing work on "Blind Signal Separation" this paper focuses on extending our previous work [1] by creating a data set that can successfully perform blind separation of polyphonic signals containing similar instruments playing similar notes in a noisy environment. Upon isolating and subtracting the dominant signal from a base signal containing varying types and amounts of noise, even though we purposefully excluded any identical matches in the dataset, the signal separation system successfully built a resulting foreign set of synthesized sounds that the classifier correctly recognized. Herein, this paper presents a system that classifies and separates two harmonic signals with added noise. This novel methodology incorporates Knowledge Discovery, MPEG7-based segmentation and Inverse Fourier Transforms. [ABSTRACT FROM AUTHOR]

This paper aims at studying the possibility of exploiting the Intelligent Agent technology in e-government for supporting the decision making activity of government agencies. Specifically, it proposes a system to assist managers of a government agency, who plan to propose a new service, to identify those citizens that could gain the highest benefit from it. The paper illustrates the proposed system and reports some experimental results. [ABSTRACT FROM AUTHOR]

Recently, many researchers have focused on designing generic and reusable middlewares to overcome the complexity in building adaptive systems. There is a general agreement that the openness provided by component-based approaches coupled with reflection mechanisms is the minimum prerequisites for supporting dynamic reconfigurations. However, this is not sufficient to implement the heart of the adaptation loop namely the decision making on the required reconfiguration that adapts the system in a given context. In this regard, this paper proposes a planning framework that subsumes and automates the adaptation decision-making in reflective component-based adaptive systems. The salient feature of this framework is to model the variability of the adaptive system as a set of variation points at which alternative component compositions and implementations can be selected to form an application configuration. The selection of a feasible configuration in a given context is based on the concept of component wise utility functions that estimates the user benefit of including a specific implementation alternative at a variation point in the composition. We show that the selection problem can be modelled as a multi constraint shortest path that can be found in polynomial time. Our approach is validated through a real world example implementing adaptive scenarios in the domain of mobile computing. [ABSTRACT FROM AUTHOR]

This paper proposes the use of Selective Acknowledgements (SACK) from clients to services as a method for reducing the memory footprint of replicated services. The paper discusses the general concept of SACK in replicated services and presents a specific implementation of SACK for an existing replication infrastructure. Performance measurements exhibiting the effectiveness of SACK are also presented. [ABSTRACT FROM AUTHOR]

Querying about the time-varying locations of moving objects is particularly cumbersome in environments composed of a very large number of distributed spatio-temporal database servers. In particular, searching for a specific object can require to visit each server. In this paper we propose a strategy to avoid such an exhaustive search that is based on the use of a centralized index, called meta-index, which is the entry point for spatio-temporal search queries. This index allows a software agent to determine a search plan for visiting the most likely servers to contain the target object. An important issue for large and dynamic distributed servers systems is to keep the meta-index as up-to-date as possible with the real system. This paper defines and compares two different strategies for maintaining properly updated the meta-index: crawling, where the centralized system that keeps the index controls itself the updating process, and harvesting, where each distributed database server autonomously transfers data directly into the central index system. Both strategies were implemented and compared by using discrete-event simulators with demanding synthetic spatio-temporal data. The results show that crawling has better performance. [ABSTRACT FROM AUTHOR]

Data grid is developed to facilitate sharing data and resources located in different parts of the world. The major barrier to support fast data access in a data grid is the high latency of wide area networks and the Internet. Data replication is adopted to improve data access performance. When different sites hold replicas, there are significant benefits while selecting the best replica. In this paper, we propose a new replica selection strategy based on classification techniques. In this strategy the replica selection problem is regarded as a classification problem. The data transfer history is utilized to help predicting the best site holding the replica. The adoption of the switch mechanism of replica selection model avoids a waste of time for inaccurate classification results. In this paper, we study and simulate KNN and SVM methods for different file access patterns and compare results with the traditional replica catalog model. The results show that our replica selection model outperforms the traditional one for certain file access requests. [ABSTRACT FROM AUTHOR]

Evolutionary Algorithms (EA) are well suited for solving optimisation problems, especially NP-complete problems. This paper presents the application of the Evolutionary Algorithm GLEAM ( General Learning and Evolutionary Algorithm and Method) in the field of grid computing. Here, grid resources like computing power, software, or storage have to be allocated to jobs that are running in heterogeneous computing environments. The problem is similar to industrial resource scheduling, but has additional characteristics like co-scheduling and high dynamics within the resource pool and the set of requesting jobs. The paper describes the deployment of GLEAM in the global optimising grid resource broker GORBA (Global Optimising Resource Broker and Allocator) and the first promising results in a grid simulation environment. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a mobile services environment, namely SMOP, in which semantic web based service capability matching and location-aware information gathering are both used to develop mobile applications. Domain independency and support on semantic matching in mobile service capabilities are the innovative features of the proposed environment. Built-in semantic matching engine of the environment provides the addition of new service domain ontologies which is critical in terms of system extensibility. Therefore the environment is generic in terms of developing various mobile applications and provides most relevant services for mobile users by applying semantic capability matching in service lookups. GPS (Global Positioning System) and map service utilization cause to find near services in addition to capability relevancy. The software architecture and system extensibility support of the environment are discussed in the paper. The real life implementation of the environment for the estate domain is also given as a case study in the evaluation section of the paper. [ABSTRACT FROM AUTHOR]

Access to multisource heterogeneous data is a fundamental research issue in a variety of contexts, including syndicated data retrieval, Web service selection and cooperative information systems. In these variable contexts, the brokering approach to multisource data access provides greater flexibility with respect to the more traditional data integration. The general brokering model assumes that the broker is submitted a query and has the responsibility to optimize the response along specified parameters such as time efficiency, completeness, and consistency. This paper takes a data quality perspective on data brokering and considers data accuracy. Furthermore, the data quality literature assumes that metadata are associated with data to describe their quality. Metadata support data selection without viewing and assessing data directly. On the contrary, previous brokering approaches view data. This paper compares previous results with those of a brokering approach based on metadata which assumes that actual data are transparent to the broker. Testing results comparing the delta between the data visibility and transparency approaches to data brokering are presented. [ABSTRACT FROM AUTHOR]

Process-aware information systems have to be frequently adapted due to business process changes. One important challenge not adequately addressed so far concerns the evolution of process choreographies, i.e., the change of interactions between partner processes in a cross-organizational setting. If respective modifications are applied in an uncontrolled manner, inconsistencies or errors might occur in the sequel. In particular, modifications of private processes performed by a single party may affect the implementation of the private processes of partners as well. In this paper we present the DYCHOR (DYnamic CHOReographies) framework which allows process engineers to detect how changes of private processes may affect related public views and - if so - how they can be propagated to the public and private processes of partners. In particular, DYCHOR exploits the semantics of the applied changes in order to automatically determine the adaptations necessary for the partner processes. Altogether our framework provides an important contribution towards the realization of adaptive, cross-organizational processes. [ABSTRACT FROM AUTHOR]

The problem of sharing data in peer-to-peer environment has received considerable attention in recent years. However, knowledge sharing in peer architectures has received very little attention. This paper proposes a framework for query reformulation in peer architectures. We first consider a mapping language based on a particular description logic that includes class connectors. Then a set of rules are proposed for building graphs. Because the axioms in a knowledge base have different properties, our graph generation algorithm classifies the generated graphs into four sets (Ugraph, Bgraph, Cgraph and Dgraph). Furthermore, based on the properties of the unification nodes, our algorithms can reformulate each kind of atom in a special way. Finally we do extensive simulation experiments and simulation results show that the proposed method has better performance than those of Mork's [8]. [ABSTRACT FROM AUTHOR]

There is a critical need to automatically manage large volumes of scientific data and applications in scientific workflows. Database technologies seem to be well suited to handle highly complex data managements. However, most of the workflow management systems (WFMSs) only utilize database technologies to a limited extent. In this paper, we present a DB-integrated scientific workflow framework which adopts the object deputy model to describe the execution of a series of scientific tasks. This framework allows WFMS management operations to be performed in a way analogous to traditional data management operations. Most important of all, data provenance method of this framework can provide much higher performance than other methods. Three kinds of schemas for data provenance are proposed and performance for each schema is analyzed in this paper. [ABSTRACT FROM AUTHOR]

To ensure the efficiency of data exchange between simulation members via multicast groups in the simulation system based on High Level Architecture (HLA), this paper proposes a novel method of dynamic data distribution based on publication and subscription tree (PS-Tree). The main contributions of this paper include: (1) Proposing the structure of PS-Tree which can manifest the relationship of data exchange between simulation members. (2) Describing the method of dynamic data distribution based on PS-Tree by mining association rule and (3) Analyzing the performance. Experiment shows that this dynamic data distribution method can implement data distribution efficiently and effectively. [ABSTRACT FROM AUTHOR]

Time series similarity search is of growing importance in many applications. Wavelet transforms are used as a dimensionality reduction technique to permit efficient similarity search over high-dimensional time series data. This paper proposes the tight upper and lower bounds on the estimation distance using wavelet transform, and we show that the traditional distance estimation is only part of our lower bound. According to the lower bound, we can exclude more dissimilar time series than traditional method. And according to the upper bound, we can directly judge whether two time series are similar, and further reduce the number of time series to process in original time domain. The experiments have shown that using the upper and lower tight bounds can significantly improve filter efficiency and reduce running time than traditional method. [ABSTRACT FROM AUTHOR]

For addressing the growing problem of junk E-mail on the Internet, this paper proposes an effective E-mail classifying technique. Our work handles E-mail messages as semi-structured documents consisting of a set of fields with predefined semantics and a number of variable length free-text contents. The main contributions of this paper include the following: First, we present a Support Vector Machine (SVM) based model that incorporates the Principal Component Analysis (PCA) technique to reduce the data in terms of size and dimensionality of the input feature space. As a result, the input data become classifiable with fewer features, and the training process has faster convergence speed. Second, we build the classification model using both the $\mathcal{C}$-support vector machine and v-support vector machine algorithms. Various control parameters for performance tuning are studied in an extensive set of experiments. The results of our performance evaluation indicate that the proposed technique is effective in E-mail classification. [ABSTRACT FROM AUTHOR]

This paper discusses the object placement and caching strategies on the AN.P2P, which is an Application Networking infrastructure to implement pervasive content delivery on the peer-to-peer networks. In general, the AN.P2P allows the peer to send the content's original object with the associated content adaptation workflow to other peers. The recipient peer can reuse the original object to generate different content presentations. In order to achieve reasonable performance, this paper proposes several object placement schemes and caching strategies. Our simulation results show that these methods could effectively improve the system performance in terms of query hops, computation cost and retrieval latency. [ABSTRACT FROM AUTHOR]

This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words/phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method. [ABSTRACT FROM AUTHOR]

Web contents repurposing and personalization is becoming crucial for enabling ubiquitous Web access from a wide range of mobile devices under varying conditions that may depend on device capabilities, network connectivity, navigation context, user preferences, user disabilities and existing social conventions. Semantic annotations can provide additional information so that a content adaptation engine, based on the holistic integration of both Information Agents and Semantic Web technologies, can make better decisions, leading to optimal results in terms of legibility and usability. Bearing this in mind, this paper presents the rationale behind MorfeoSMC: an open source mobility platform that enables the development of semantics-aware mobile applications and services in order to provide improved Web accessibility and increase social inclusion. In particular, the paper focuses on how MorfeoSMC information agents tackle the use of semantic markup in the information rendered for users through the mobility platform and as part of a user-interest profile-aware and navigation context-aware Web content adaptation process. It also presents an innovative semantic matching framework that is at the core of this semantics-aware Web content adaptation process. [ABSTRACT FROM AUTHOR]

XML data usually consists of tree-structured hierarchical data, which affects the storing and searching mechanisms for XML. When storing XML data into databases the hierarchical relationships among XML nodes need to be considered. User's search queries that specify hierarchical relationships among the nodes also require appropriate processing mechanisms. Structural join operations provide a solution to this problem by efficiently computing hierarchical relationships in XML databases based on the node numbering storage scheme. However, in order to process a branch query containing several hierarchical relationships on XML data, many structural joins need to be sequentially carried out and result in a high query execution cost. This paper proposes mechanisms to reduce the cost of processing branch pattern XML queries requiring multiple structural joins. We discuss two approaches for rewriting a query composed of a single branch, and then apply these approaches to general branch queries. The first approach uses the concept of equivalence class relationships among regular path expression queries. The second approach uses a bottom-up approach to reduce the overhead identified in the first scheme. Experimental results show that the proposed schemes can reduce the query execution cost by up to an order of magnitude of the original execution cost. [ABSTRACT FROM AUTHOR]

The web services paradigm has enabled an increasing number of providers to deploy and host autonomic and remotely accessible services. However, the true potential of such a distributed infrastructure can only be reached when such autonomic services can be combined together as parts of a workflow, in order to collectively achieve combined functionality. In this paper we present our work in the area of automatic workflow composition among web services with semantically described functionality capabilities. For that purpose, we are using a set of heuristics derived from the connectivity structure of the service repository in order to effectively guide the composition process. The methodologies described in this paper have been inspired by research in areas such as citation analysis and bibliometrics. In addition, we present comparative experimentation results in order to evaluate the presented techniques. [ABSTRACT FROM AUTHOR]

The main contribution of this paper is an initial method for automatically exploiting ontology design patterns with the aim of further automating the creation of enterprise ontologies in small-scale application contexts. The focus is so far on developing a fully automated construction method, thereby somewhat reducing the requirements on ontology customization and level of detail. In this paper we present an approach how to use knowledge (patterns) from other areas, like data modeling, knowledge reuse, software analysis and software design, to create ontology patterns. These design patterns are then used within our method for automatically matching and pruning them, in accordance with information extracted from existing knowledge sources within the company in question. Though the method still needs some fine-tuning, it has already been used when creating an enterprise ontology for a supplier-company within the automotive industry. [ABSTRACT FROM AUTHOR]