Pinar Senkul

An architecture for workflow scheduling under resource allocation constraints

Research on specification and scheduling of workflows has concentrated on temporal and causality constraints, which specify existence and order dependencies among tasks. However, another set of constraints that specify resource allocation is also equally important. The resources in a workflow environment are agents such as person, machine, software, etc. that execute the task. Execution of a task has a cost and this may vary depending on the resources allocated in order to execute that task. Resource allocation constraints define restrictions on how to allocate resources, and scheduling under resource allocation constraints provide proper resource allocation to tasks. In this work, we provide an architecture to specify and to schedule workflows under resource allocation constraints as well as under the temporal and causality constraints. A specification language with the ability to express resources and resource allocation constraints and a scheduler module that contains a constraint solver in order to find correct resource assignments are core and novel parts of this architecture.

A logical framework for scheduling workflows under resource allocation constraints

A workflow consists of a collection of coordinated tasks designed to carry out a well-defined complex process, such as catalog ordering, trip planning, or a business process in an enterprise. Scheduling of workflows is a problem of finding a correct execution sequence for the workflow tasks, i.e., execution that obeys the constraints that embody the business logic of the workflow. Research on workflow scheduling has largely concentrated on temporal constraints, which specify correct ordering of tasks. Another important class of constraints -- those that arise from resource allocation -- has received relatively little attention in workflow modeling. Since typically resources are not limitless and cannot be shared, scheduling of a workflow execution involves decisions as to which resources to use and when. In this work, we present a framework for workflows whose correctness is given by a set of resource allocation constraints and develop techniques for scheduling such systems. Our framework integrates Concurrent Transaction Logic (CTR) with constraint logic programming (CLP), yielding a new logical formalism, which we call Concurrent Constraint Transaction Logic, or CCTR.

Composing semantic Web services under constraints

Web service composition is an inevitable aspect of Web services technology, which solves complex problems by combining available basic services and ordering them to best suit the problem requirements. Automatic composition gives us flexibility of selecting best candidate services at composition time satisfying QoS requirements; this would require the user to define constraints for selecting and composing candidate Web services. In this work, a Web service composition approach is presented in which a rich set of constraints can be defined on the composite service. The output of the framework is the schedule of Web service composition in which how and when services are executed is defined. The basic properties of the proposed approach is converting Web service composition problem into a constraint satisfaction problem in order to find the best solution that meets all criteria defined by user and providing semantic compatibility and composability during composition.

Improving pattern quality in web usage mining by using semantic information

Frequent Web navigation patterns generated by using Web usage mining techniques provide valuable information for several applications such as Web site restructuring and recommendation. In conventional Web usage mining, semantic information of the Web page content does not take part in the pattern generation process. In this work, we investigate the effect of semantic information on the patterns generated for Web usage mining in the form of frequent sequences. To this aim, we developed a technique and a framework for integrating semantic information into Web navigation pattern generation process, where frequent navigational patterns are composed of ontology instances instead of Web page addresses. The quality of the generated patterns is measured through an evaluation mechanism involving Web page recommendation. Experimental results show that more accurate recommendations can be obtained by including semantic information in navigation pattern generation, which indicates the increase in pattern quality.

Logic-Based Approaches to Workflow Modeling and Verification

A workflow is a collection of coordinated activities designed to carry out a well-defined complex process, such as trip planning, student registration, or a business process in a large enterprise. An activity in a workflow might be performed by a human, a device, or a program. Workflow management systems (or WfMS) provide a framework for capturing the interaction among the activities in a workflow and are recognized as a new paradigm for integrating disparate systems, including legacy systems. A large workflow system might involve many disparate activities that are coordinated in complex ways and are subject to many constraints. Thus, modeling such systems and ensuring that they perform according to the specifications is not an easy task. To be able to analyze the properties of workflows, the latter must be specified using a formalism with well-defined semantics. The popular formalisms in this area are the various logics, Petri Nets [1,35], Event-Condition-Action rules [23,15], and State Charts [36]. In this chapter we survey and compare a number of logic-based formalisms that were proposed in the literature.

Concept discovery on relational databases: New techniques for search space pruning and rule quality improvement

Multi-relational data mining has become popular due to the limitations of propositional problem definition in structured domains and the tendency of storing data in relational databases. Several relational knowledge discovery systems have been developed employing various search strategies, heuristics, language pattern limitations and hypothesis evaluation criteria, in order to cope with intractably large search space and to be able to generate high-quality patterns. In this work, we introduce an ILP-based concept discovery framework named Concept Rule Induction System (CRIS) which includes new approaches for search space pruning and new features, such as defining aggregate predicates and handling numeric attributes, for rule quality improvement. In CRIS, all target instances are considered together, which leads to construction of more descriptive rules for the concept. This property also makes it possible to use aggregate predicates more accurately in concept rule construction. Moreover, it facilitates construction of transitive rules. A set of experiments is conducted in order to evaluate the performance of proposed method in terms of accuracy and coverage.

Using semantic information for web usage mining based recommendation

Web usage mining has become popular in various business areas related with Web site development. In Web usage mining, commonly visited navigational paths are extracted in terms of Web page addresses from the Web server visit logs, and the patterns are used in various applications including recommendation. The semantic information of the Web page contents is generally not included in Web usage mining. In this work, a framework for integrating semantic information with Web usage mining is presented. The frequent navigational patterns are extracted in the form of ontology instances instead of Web page addresses and the result is used for generating Web page recommendations to the visitor. In addition, an evaluation mechanism is implemented in order to test the success of the recommendation. Test results show that more accurate recommendations can be obtained by including semantic information in the Web usage mining.

A Framework for Sentiment Analysis in Turkish: Application to Polarity Detection of Movie Reviews in Turkish

In this work, we present a framework for unsupervised sentiment analysis in Turkish text documents. As part of our framework, we customize the SentiStrength sentiment analysis library by translating its lexicon to Turkish. We apply our framework to the problem of classifying the polarity of movie reviews. For performance evaluation, we use a large corpus of Turkish movie reviews obtained from a popular Turkish social media site. Although our framework is unsupervised, it is demonstrated to achieve a fairly good classification accuracy, approaching the performance of supervised polarity classification techniques.

A Workflow-Based Web Service Composition System

In this work, we present a Web service composition system that provides an environment for specifying the flow model of the composite service and the users constraints on the composite service. We adopt our previous work on workflow modeling and scheduling under resource allocation constraints for Web service composition and extend it with service discovery functionalities. In modeling the composite service, the system guides the user for selecting the relevant service types. In both guiding functionality and the service discovery phase, ontology-based semantic information of the services is utilized

Semantic Expansion of Tweet Contents for Enhanced Event Detection in Twitter

This paper aims to enhance event detection methods in a micro-blogging platform, namely Twitter. The enhancement technique we propose is based on lexico-semantic expansion of tweet contents while applying document similarity and clustering algorithms. Considering the length limitations and idiosyncratic spelling in Twitter environment, it is possible to take advantage of word similarities and to enrich texts with similar words. The semantic expansion technique we implement is based on syntagmatic and paradigmatic relationships between words, extracted from their co-occurrence statistics. As our technique does not depend on an existing ontology or a lexicon database such as Word Net, it should be applicable for any language. The proposed technique is applied on a tweet set collected for three days from the users in Turkey. The results indicate earlier detection of events and improvements in accuracy.