A web services framework for distributed model management

Madhusudan, Therani

doi:10.1007/s10796-006-9015-2

Cited by 17 publications

(4 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A key technique of the proposed approach is the use of an interpreter to generate and interpret messages in the exchange language and to manage the path of the dialogue. Madhusudan (2007) presented a web services framework for distributed model management based on an approach for web services integration called Integrated Service Planning and Execution. An important component of the proposed framework is model composition by means of "hierarchical task networks."…”

Section: Introduction To Papers In This Special Issuementioning

confidence: 99%

Services computing as the foundation of enterprise agility: Overview of recent advances and introduction to the special issue

2007

View full text Add to dashboard Cite

The advancement of web services in the last few years has spurred a number of revolutionary concepts in information technology and management including serviceoriented architectures, service-oriented computing, and services science, management and engineering, which can be collectively called as "services computing." Services computing is a new research field that goes beyond traditional computing disciplines as it includes not only architectural, programming, deployment, and other engineering issues, but also management issues such as business component modeling, business process design, and service delivery. In this paper, we provide an overview of emerging research concepts in services computing without attempting to unify them as it will take sometime for the field to become mature. In addition, we take a position that the ultimate goal of services computing is to create the necessary technological and managerial foundation to support enterprise agility. In this short paper, we give an overview of services computing, describe its relationship to enterprise agility, and discuss basic technical and managerial issues. Finally, we introduce the papers that are published in this special issue.

show abstract

Section: Introduction To Papers In This Special Issuementioning

confidence: 99%

Services computing as the foundation of enterprise agility: Overview of recent advances and introduction to the special issue

2007

View full text Add to dashboard Cite

show abstract

“…A model template is a refinement of a model type such as a production planning LP model, and a model instance is an instance of a model template in which the source of values for each parameter has been declared. Model templates have been represented using key-value pairs and filter lists in (Chari 2002), as Web Services Description Language (WSDL) service descriptors (Madhusudan 2007), and as OWL (XML-based) model profiles (Bhrammanee and Wuwongse 2008). Metagraphs (Basu and Blanning 1994a;Basu et al 1997), a specialized type of graph structure, has been the significant development in this area.…”

Section: Model Management-iimentioning

confidence: 99%

“…In addition to model composition (Basu and Blanning 1994b), the metagraph construct enables the representation of and reasoning with metadata such as assumptions associated with models (Basu, Blanning and Shtub, 1998). Work within the last ten years has focused on automating model composition and execution process, and combining partial solutions from multiple composite models and databases as in Chari (2002), leveraging XML in model composition (Bhrammanee and Wuwongse 2008) and implementing model composition through a sequence of web service invocations as in the WEBOPT project (Valente and Mitra 2007), and in (Madhusudan 2007).…”

Section: Model Management-iimentioning

confidence: 99%

Machine Learning

2013

Encyclopedia of Operations Research and Management Science

View full text Add to dashboard Cite

A term used in the artificial intelligence community to indicate automated improvement based on experience or empirical data in accomplishing a given task such as optimizing an objective function. MaintenanceMaintenance is the support of successful system operation during long periods of usage by means of: (1) regular or sample check-ups; (2) planned or preventive replacement of the system's units; (3) failure diagnosis; and/or (4) spare units supply. Operations research models for a system maintenance analysis are represented mainly by optimization models for the improvement of system and equipment reliability.For (1) and (2), one usually uses methods of controlled stochastic processes. For (3), one uses special methods based on mathematical logic, while (4) is considered in the scope of optimal redundancy and inventory control. IntroductionManpower (or, human resource) planning is concerned with the quantitative aspects of the supply of and demand for people in employment. At one extreme this might include the whole working population of a country, but it has been most successful when applied to smaller, more homogeneous systems like individual firms or professions. The term manpower planning appears to date from the 1960s though many of the ideas can be traced back much further. In recent years terms such as Workforce Panning and Personnel Planning have been used in the same sense. A history of the subject up to the 1980s,from a U.K. perspective, will be found in Smith and Bartholomew (1988). The literature of the subject is very scattered reflecting the diverse disciplinary origins of the practitioners, but most of the technical material is to be found in the journals of operations research, probability, and statistics. There was an initial surge of publication in the late 1960s and early 1970s and since then book length treatments include Grinold and Marshall (1977), Vajda (1978), and Bennison and Casson (1984). Bartholomew, Forbes and McClean (1991) gives a thorough coverage of the technical material and contains an extensive bibliography. Since then there has been a period of consolidation. The earlier theoretical work has largely proved adequate for practical needs, though there have been developments in closely related areas. See, for example, Kalamatianou and McLean (2003). The essence of manpower planning is summed up in the aphorism that its aim is to have the right numbers of people of the right kinds in the right places at the right time. The basic approach is first to classify the members of a system in relevant ways. These will often be on the basis of such things as grade, salary level, sex, qualifications, and job title. The state of the system at any point in time can then be described by the numbers in these categories, often referred to as the stocks. Over time, changes occur as individuals join, leave the system or move within it. The numbers making these transitions are called the flows. The factors giving rise to change may be predictable or unpredictable but will include such things ...

show abstract

“…Iyer et al (2005) recently proposed a web services architecture for model sharing and reuse of spreadsheet models while Ezechukwu and Maros (2003) proposes an architecture for supporting distributed optimization over the Internet. Recently, Madhusudan (2007) presented a framework for distributed model management based on web services. The framework utilizes the integrated Service Planning and Execution (ISP&E) (Madhusudan and Uttamsingh 2006) for composing web services.…”

Section: Model Management For Decision Supportmentioning

confidence: 99%

Decision-enabled dynamic process management for networked enterprises

Deokar

El‐Gayar

2010

Inf Syst Front

View full text Add to dashboard Cite

Enterprises in today's networked economy face numerous information management challenges, both from a process management perspective as well as a decision support perspective. While there have been significant relevant advances in the areas of business process management as well as decision sciences, several open research issues exist. In this paper, we highlight the following key challenges. First, current process modeling and management techniques lack in providing a seamless integration of decision models and tools in existing business processes, which is critical to achieve organizational objectives. Second, given the dynamic nature of business processes in networked enterprises, process management approaches that enable organizations to react to business process changes in an agile manner are required. Third, current state-of-the-art decision model management techniques are not particularly amenable to distributed settings in networked enterprises, which limits the sharing and reuse of models in different contexts, including their utility within managing business processes. In this paper, we present a framework for decision-enabled dynamic process management that addresses these challenges. The framework builds on computational formalisms, including the structured modeling paradigm for representing decision models, and hierarchical task networks from the artificial intelligence (AI) planning area for process modeling. Within the framework, interleaved process planning (modeling), execution and monitoring for dynamic process management throughout the process lifecycle is proposed. A serviceoriented architecture combined with advances from the semantic Web field for model management support within business processes is proposed.

show abstract

A web services framework for distributed model management

Cited by 17 publications

References 42 publications

Services computing as the foundation of enterprise agility: Overview of recent advances and introduction to the special issue

Services computing as the foundation of enterprise agility: Overview of recent advances and introduction to the special issue

Machine Learning

Decision-enabled dynamic process management for networked enterprises

Contact Info

Product

Resources

About