Mark D. Feblowitz

A Folksonomy-Based Model of Web Services for Discovery and Automatic Composition

In this paper, we propose a novel way of modeling Web services using folksonomies. The key advantage of our model is that it allows a large number of users to participate, easily, in annotating services with tags. This is in contrast to more expressive, logic based models of services, such as semantic Web service models, which require significant expertise for annotation and maintenance. Our folksonomy-based model allows associating semantic constraints on the input and output messages of web service operations using tags obtained from a folksonomy. We show how the model can be used for discovery and composition of services. We also describe a planner that uses this model to compose services and create workflows, automatically. We present performance results for the planner and our experiences in using this model in a sample real-world domain.

A semantics-based middleware for utilizing heterogeneous sensor networks

With the proliferation of various kinds of sensor networks, we will see large amounts of heterogeneous data. They have different characteristics such as data content, formats, modality and quality. Existing research has largely focused on issues related to individual sensor networks; how to make use of diverse data beyond the individual network level is largely unaddressed. In this paper, we propose a semantics-based approach for this problem and describe a system that constructs applications that utilize many sources of data simultaneously. We propose models to formally describe the semantics of data sources, and processing modules that perform various kinds of operations on data. Based on such formal semantics, our system composes data sources and processing modules together in response to users queries. The semantics provides a common ground such that data sources and processing modules from various parties can be shared and reused among applications. We describe our system architecture, illustrate application deployment, and share our experiences in the semantic approach.

A tag-based approach for the design and composition of information processing applications

In the realm of component-based software systems, pursuers of the holy grail of automated application composition face many significant challenges. In this paper we argue that, while the general problem of automated composition in response to high-level goal statements is indeed very difficult to solve, we can realize composition in a restricted context, supporting varying degrees of manual to automated assembly for specific types of applications. We propose a novel paradigm for composition in flow-based information processing systems, where application design and component development are facilitated by the pervasive use of faceted, tag-based descriptions of processing goals, of component capabilities, and of structural patterns of families of application. The facets and tags represent different dimensions of both data and processing, where each facet is modeled as a finite set of tags that are defined in a controlled folksonomy. All data flowing through the system, as well as the functional capabilities of components are described using tags. A customized AI planner is used to automatically build an application, in the form of a flow of components, given a high-level goal specification in the form of a set of tags. End-users use an automatically populated faceted search and navigation mechanism to construct these high-level goals. We also propose a novel software engineering methodology to design and develop a set of reusable, well-described components that can be assembled into a variety of applications. With examples from a case study in the Financial Services domain, we demonstrate that composition using a faceted, tag-based application design is not only possible, but also extremely useful in helping end-users create situational applications from a wide variety of available components.

Stream Processing Based Intelligent Transport Systems

In this paper, we present a Fleet Management Center application implemented using a stream processing infrastructure we call System S. System S enables the deployment of large scale applications with mechanisms for sharing multi-party data sources, software components, and even intermediate results. This approach significantly reduces the cost of software integration, and ownership, the major factor in Intelligent Transportation Systems. In addition, the system includes an adaptive data source management that determines the list of relevant data sources based on the current locations of the entities monitored or managed by the applications.

Data Stream Processing Infrastructure for Intelligent Transport Systems

Intelligence Transportation Systems are critical to improve the efficiency of modern transportation. A system that is flexible and powerful enough to handle diverse demands from a large user base, is still elusive. Studies have shown that developing and integrating the various components constitute a significant portion of the capital cost and complexity of such systems. In this paper, we present a stream processing infrastructure we call System S. System S enables the deployment of large scale applications. It supports a mechanism for sharing data sources, software components, and even intermediate results allowing a reduction in the cost of software integration, and ownership. We experiment the stream processing infrastructure with a Fleet Management Center, and demonstrate how the infrastructure can be used to address unique issues in traffic management.

Toward an integrative software infrastructure for water management in the smarter planet

Building a Smarter Planet™ requires creating an intelligent infrastructure that integrates technology with business, government, and the everyday life of the citizens of earth, to maximize the use of scarce resources, balance human use and ecosystem preservation, reduce costs, and improve quality of life. One of the keystones of this intelligent infrastructure is an integrative modeling framework (IMF), which is a platform for enabling the integration by nonexpert users of diverse sensor-based data, related business data, and complex cross-disciplinary mathematical modeling, in support of planning, monitoring, management, reporting, and decision support applications. We describe a research prototype that applies the Mashup Automation with Runtime Invocation and Orchestration (MARIO) technology from IBM Research to this problem in a specific application area in water management: simulating stream discharges using compositions of hydrologic process submodels derived from monolithic stream discharge simulators. We show how MARIOs semantic tagging and model composition engine enable us to meet three critical challenges of an IMF: 1) generating valid chains or compositions of model components, given a definition of starting and ending states; 2) allowing all scientifically valid compositions of components; and 3) disallowing compositions that are scientifically invalid, i.e., that combine model components whose basic assumptions about quantities such as soil architectures or evaporation schemes conflict. While we focus here on water management, the technology that we describe can readily be generalized to other intelligent infrastructure applications, e.g., cities, transportation, and utilities.

A Faceted Requirements-Driven Approach to Service Design and Composition

The Web services research community has proposed a number of approaches for service composition, ranging from manual to semi-automatic to completely automatic. However, it is often difficult to take independently developed services and compose them, since they may not work together correctly. For service composition to occur, the services in question must be designed and developed in a manner that facilitates their composition. In this paper, we propose a novel approach for service design and composition that combines top-down and bottom-up elements. Our approach is driven by faceted, tag-based functional requirements provided by end-users. These requirements describe, at a high-level, the families of compositions that end-users desire. The requirements kick off a top-down service development lifecycle, where enterprise architects and service developers design, develop and test workflows and services, possibly reusing existing flows and services in the process. At runtime, end-users can specify goals, which are satisfied through a bottom-up composition of flows from the available services. The composed flows include those explicitly designed by the architects as well as new ones that are assembled in a serendipitous manner from the available services. With examples from a case study in the financial services domain, we demonstrate our approach for designing and developing services that can be composed into myriad workflows based on end-user goals.

Semantic Matching, Propagation and Transformation for Composition in Component-Based Systems

Composition of software applications from component parts in response to high-level goals is a long-standing and highly challenging goal. We target the problem of composition in flow-based information processing systems and demonstrate how application composition and component development can be facilitated by the use of semantically described application metadata. The semantic metadata describe both the data flowing through each application and the processing performed in the associated application code. In this paper, we explore some of the key features of the semantic model, including the matching of outputs to input requirements, and the transformation and the propagation of semantic properties by components.

IBM Scenario Planning Advisor: Plan Recognition as AI Planning in Practice

We present the IBM Research Scenario Planning Advisor (SPA), a decision support system that allows users to generate diverse alternate scenarios of the future and enhance their ability to imagine the different possible outcomes, including unlikely but potentially impactful futures. The system includes tooling for experts to intuitively encode their domain knowledge, and uses AI Planning to reason about this knowledge and the current state of the world, including news and social media, when generating scenarios.