Rama Akkiraju

Dynamic Workflow Composition: Using Markov Decision Processes

The advent of Web services has made automated workflow composition relevant to Web-based applications. One technique that has received some attention for automatically composing workflows is AI-based classical planning. However, workflows generated by classical planning algorithms suffer from the paradoxical assumption of deterministic behavior of Web services, then requiring the additional overhead of execution monitoring to recover from unexpected behavior of services due to service failures, and the dynamic nature of real-world environments. To address these concerns, we propose using Markov decision processes (MDPs) to model workflow composition. To account for the uncertainty over the true environmental model, and for dynamic environments, we interleave MDP-based workflow generation and Bayesian model learning. Consequently, our method models both the inherent stochastic nature of Web services and the dynamic nature of the environment. Our algorithm produces workflows that are robust to non-deterministic behaviors of Web services and that adapt to a changing environment. We use a supply chain scenario to demonstrate our method and provide empirical results.

Mashup Advisor: A Recommendation Tool for Mashup Development

Mashup editors, like Yahoo Pipes and IBM Lotus Mashup Maker, allow non-programmer end-users to ldquomash-uprdquo information sources and services to meet their information needs. However, with the increasing number of services, information sources and complex operations like filtering and joining, even an easy to use editor is not sufficient. MashupAdvisor aims to assist mashup creators to build higher quality mashups in less time. Based on the current state of a mashup, the MashupAdvisor quietly suggests outputs (goals) that the user might want to include in the final mashup. MashupAdvisor exploits a repository of mashups to estimate the popularity of specific outputs, and makes suggestions using the conditional probability that an output will be included, given the current state of the mashup. When a suggestion is accepted, MashupAdvisor uses a semantic matching algorithm and a metric planner to modify the mashup to produce the suggested output. Our prototype was implemented on top of IBM Lotus MashupMaker and our initial results show that it is effective.

Searching service repositories by combining semantic and ontological matching

In this paper, we explore the use of domain-independent and domain-specific ontologies to find matching service descriptions. The domain-independent relationships are derived using an English thesaurus after tokenization and part-of-speech tagging. The domain-specific ontological similarity is derived by an inference on the semantic annotations associated with Web service descriptions. Matches due to the two cues are combined to determine an overall semantic similarity score. By combining multiple cues, we show that better relevancy results can be obtained for service matches from a large repository, than could be obtained using any one cue alone.

SEMAPLAN: Combining Planning with Semantic Matching to Achieve Web Service Composition

In this paper, we present a novel algorithm to compose Web services in the presence of semantic ambiguity by combining semantic matching and AI planning algorithms. Specifically, we use cues from domain-independent and domain-specific ontologies to compute an overall semantic similarity score between ambiguous terms. This semantic similarity score is used by AI planning algorithms to guide the searching process when composing services. Experimental results indicate that planning with semantic matching produces better results than planning or semantic matching alone. The solution is suitable for semi-automated composition tools or directory browsers

External matching in UDDI

As an industry-backed registry for Web Services, UDDI plays an important role in helping requesters find suitable services. Unfortunately, the current search functions in UDDI are limited in their support for making automatic service selection decisions. While some approaches have been suggested to enhance the search capabilities in UDDI using service semantics, they suffer from limitations. These approaches either prescribe performing semantic matching outside of UDDI leaving its search function unchanged or propose embedding a specific matching engine within UDDI thereby making the search function inflexible. In this work, we present a flexible mechanism to enhance UDDI search function. Using our approach, users can integrate multiple external matching services with a UDDI registry to support multiple external service description languages. The result is a UDDI registry with flexible and intelligent service search function that can be used for dynamic service selection.

An Agent-Based Approach for Scheduling Multiple Machines

We present a new agent-based solution approach for the problem of scheduling multiple non-identical machines in the face of sequence dependent setups, job machine restrictions, batch size preferences, fixed costs of assigning jobs to machines and downstream considerations. We consider multiple objectives such as minimizing (weighted) earliness and tardiness, and minimizing job-machine assignment costs. We use an agent-based architecture called Asynchronous Team (A-Team), in which each agent encapsulates a different problem solving strategy and agents cooperate by exchanging results. Computational experiments on large instances of real-world scheduling problems show that the results obtained by this approach are significantly better than any single algorithm or the scheduler alone. This approach has been successfully implemented in an industrial scheduling system.

Shared Services Transformation: Conceptualization and Valuation from the Perspective of Real Options

In todays volatile global economy, where many organizations face severe pressure to downsize, the “shared services” model, in which a firm merges common functions performed by multiple units into a single service delivery organization, provides an innovative approach to make business more efficient and effective. To successfully implement shared services, firms need to strategically decide whether and how to pursue various service transformation alternatives such as simplification, standardization, consolidation, insourcing, or outsourcing. In this study, we develop the notion of real options into a unique theoretical lens for conceptualizing service organizations and their transformation in an uncertain business environment. Specifically, we view service organization as a set of strategic options that give the firm preferential access to future transformation opportunities. We create a taxonomy of these options, and introduce a decision methodology for valuing alternative shared services transformation approaches. We illustrate this methodology by applying it in a real business case to justify a global firms decision regarding the transformation of its finance organization.

Cooperative Multiobjective Decision Support for the Paper Industry

We built and deployed a decision-support system for scheduling paper manufacturing and distribution, an extremely complex task with multiple stages of production and strong interaction between stages. In contrast to earlier approaches, our system considers multiple scheduling objectives and multiple stages of production and distribution simultaneously using multiple evaluation criteria. Our system functions as an intelligent assistant to the schedulers and generates multiple good scheduling alternatives using a portfolio of algorithms and direct human-expert input. The successful deployment of our system at several paper mills in North America has resulted insignificant savings, greater customer satisfaction, and improved business processes.

Dynamic workflow composition using Markov decision processes

The advent of Web services has made automated workflow composition relevant to Web based applications. One technique, that has received some attention, for automatically composing workflows is AI-based classical planning. However, classical planning suffers from the paradox of first assuming deterministic behavior of Web services, then requiring the additional overhead of execution monitoring to recover from unexpected behavior of services. To address these concerns, we propose using Markov decision processes (MDPs), to model workflow composition. Our method models both, the inherent stochastic nature of Web services, and the dynamic nature of the environment. The resulting workflows are robust to nondeterministic behaviors of Web services and adaptive to a changing environment. Using an example scenario, we demonstrate our method and provide empirical results in its support.

Learning Ontologies to Improve the Quality of Automatic Web Service Matching

Automatically finding suitable Web services given a request is a difficult problem because the interface descriptions of Web services are often terse and cryptic. Dictionary and information retrieval based techniques have proven useful in disambiguating the semantics of service descriptions, but they are limited in their capability to consider the relationships between the words describing the Web services. Current ontology-based approaches typically require a user to explicitly create domain ontologies. This paper presents a novel technique that significantly improves the quality of semantic Web service matching by (1) automatically generating ontologies based on Web service descriptions and (2) using these ontologies to guide the mapping between Web services. Our approach differs from earlier work on service matching by considering the relationship between words rather than treating them as a bag of unrelated words. The experimental results indicate that with our unsupervised approach we can eliminate up to 70% of incorrect matches that are made by dictionary-based approaches.