Dipsy Kapoor

Automatically composing data workflows with relational descriptions and shim services

Many scientific problems can be represented as computational workflows of operations that access remote data, integrate heterogeneous data, and analyze and derive new data. Even when the data access and processing operations are implemented as web or grid services, workflows are often constructed manually in languages such as BPEL. Adding semantic descriptions of the services enables automatic or mixed-initiative composition. In most previous work, these descriptions consists of semantic types for inputs and outputs of services or a type for the service as a whole. While this is certainly useful, we argue that is not enough to model and construct complex data workflows. We present a planning approach to automatically constructing data processing workflows where the inputs and outputs of services are relational descriptions in an expressive logic. Our workflow planner uses relational subsumption to connect the output of a service with the input of another. This modeling style has the advantage that adaptor services, so-called shims, can be automatically inserted into the workflow where necessary.

Building and Using a Knowledge Graph to Combat Human Trafficking

There is a huge amount of data spread across the web and stored in databases that we can use to build knowledge graphs. However, exploiting this data to build knowledge graphs is difficult due to the heterogeneity of the sources, scale of the amount of data, and noise in the data. In this paper we present an approach to building knowledge graphs by exploiting semantic technologies to reconcile the data continuously crawled from diverse sources, to scale to billions of triples extracted from the crawled content, and to support interactive queries on the data. We applied our approach, implemented in the DIG system, to the problem of combating human trafficking and deployed it to six law enforcement agencies and several non-governmental organizations to assist them with finding traffickers and helping victims.

Argos: dynamic composition of web services for goods movement analysis and planning

This Project Highlight describes Year 3 activities of our Argos research. The purpose of the research is to develop a flexible data query and analysis system based on the web services paradigm. Our application domain is metropolitan goods movement. The project began in August 2003. We seek to blend computer science and social science approaches by developing new data integration tools and applying them to social science research problems. The research has three objectives: 1) to advance computer science research by developing an expressive web services description language and techniques for dynamically composing web services, 2) to develop and conduct test applications of an intra-metropolitan goods movement flow model using web services in cooperation with government partners, and 3) to use the model to conduct social science research on intra-metropolitan economic linkages and spatial structure. The approach to web service composition is general and can be applied to other scientific data gathering and analysis tasks.

Data processing workflows in the social sciences: representation and automatic generation

Much of the work of social scientists and government practitioners is consumed by accessing, collating, and analyzing data. This is particularly true in the planning and economic modeling agencies. Unfortunately, there is a severe lack of tools to facilitate this process and much of the integration is done manually by ad-hoc methods. Moreover, raw data are of limited utility. Usually these data are the input to models of more complex phenomena that produce additional data of interest. For example, in our commodity flow domain, we derive truck traffic along specific highway links within a metropolitan area, based on quite far-removed raw (source) data such as employment, imports into and exports out of the region, etc, by using a complex workflow of operations.

The Provenance Bee Wiki: Tracking the Growth of Semantic Wiki Communities

Contributors in hundreds of semantic wiki sites are creating structured information in RDF every day, thus growing the semantic content of the Web in spades. Although wikis have been analyzed extensively, there has been little analysis of the use of semantic wikis. The Provenance Bee Wiki was created to gather and aggregate data from these sites, show how this content is growing over time, and to make all this detailed data readily available to the research community. We also present a high-level analysis of the almost 600 wikis indexed in Provenance Bee Wiki that have less than 5,000 pages.