InfoSleuth is an agent-based system that automates the gathering and analysis of dynamic, distributed data accessible over the web. To gather data, a user specifies an SQL query which references elements from a common domain ontology (i.e., terminology). Agents then locate resources that have advertised having data relevant to these elements and translate the ontology-based query into queries referencing elements in the different schemas of the identified local databases. Other agents then integrate the results returned from these multiple resources and express them in terms of the common ontology. The large amount of data returned may be overwhelming, and so analysis agents serve to filter and interpret it. InfoSleuth is implemented in Java, and includes a common agent shell and specializations of the shell. The system was developed within a research environment over the course of 5 years and is now being hardened for commercial applications.

The goal of the InfoSleuth project at MCC is to exploit and synthesize new technologies into a unified system that retrieves and processes information in an ever-changing network of information sources. InfoSleuth has its roots in the Carnot project at MCC, which specialized in integrating heterogeneous information bases. However, recent emerging technologies such as internetworking and the World Wide Web have significantly expanded the types, availability, and volume of data available to an information management system. Furthermore, in these new environments, there is no formal control over the registration of new information sources, and applications tend to be developed without complete knowledge of the resources that will be available when they are run. Federated database projects such as Carnot that do static data integration do not scale up and do not cope well with this ever-changing environment. On the other hand, recent Web technologies, based on keyword search engines, are scalable but, unlike federated databases, are incapable of accessing information based on concepts. In this experience paper, we describe the architecture, design, and implementation of a working version of InfoSleuth. We show how InfoSleuth integrates new technological developments such as agent technology, domain ontologies, brokerage, and internet computing, in support of mediated interoperation of data and services in a dynamic and open environment. We demonstrate the use of information brokering and domain ontologies as key elements for scalability.

Article Free Access Share on The InfoSleuth Project Authors: R. J. Bayardo Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , W. Bohrer Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , R. Brice Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Cichocki Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , J. Fowler Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Halal Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , V. Kashyap Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , T. Ksiezyk Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , G. Martin Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Nodine Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Rashid Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , M. Rusinkiewicz Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , R. Shea Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , C. Unnikrishnan Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , A. Unruh Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile , D. Woelk Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, Texas Microelectronics and Computer Technology Corporation (MCC), 3500 West Balcones Center Drive, Austin, TexasView Profile Authors Info & Claims SIGMOD '97: Proceedings of the 1997 ACM SIGMOD international conference on Management of dataJune 1997 Pages 543–545https://doi.org/10.1145/253260.253401Online:01 June 1997Publication History 7citation339DownloadsMetricsTotal Citations7Total Downloads339Last 12 Months2Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

This paper advances two approaches to integrating handwritten character segmentation and recognition within one system, where the underlying function is learned by a backpropagation neural network. Integrated segmentation and recognition is necessary when characters overlap or touch, or when an individual character is broken up. The first approach exhaustively scans a field of characters, effectively creating a possible segmentation at each scan point. A neural net is trained to both identify when its input window is centered over a character, and if it is, to classify the character. This approach is similar to most recently advanced approaches to integrating segmentation and recognition, and has the common flaw of generating too many possible segmentations to be truly efficient. The second approach overcomes this weakness without reducing accuracy by training a neural network to mimic the ballistic and corrective saccades (eye movements) of human vision. A single neural net learns to jump from character to character, making corrective jumps when necessary, and to classify the centered character when properly fixated. The significant aspect of this system is that the neural net learns to both control what is in its input window as well as to recognize what is in the window. High accuracy results are reported for a standard database of handprinted digits for both approaches.

Visual object recognition is often conceived of as a final step in a visual processing system, First, physical information in the raw image is used to isolate and enhance to-be-recognized clumps and then each of the resulting preprocessed representations is fed into the recognizer. This general conception fails when there are no reliable physical cues for isolating the objects, such as when objects overlap. This paper describes an approach, called centered object integrated segmentation and recognition (COISR), for integrating object segmentation and recognition within a single neural network. The application is handprinted character recognition. The approach uses a backpropagation network that scans a field of characters and is trained to recognize whether it is centered over a single character or between characters. When it is centered over a character, the net classifies the character. The approach is tested on a dataset of handprinted digits and high accuracy rates are reported.

We report on results of training backpropagation nets with samples of hand-printed digits scanned off of bank checks and hand-printed letters interactively entered into a computer through a stylus digitizer. Generalization results are reported as a function of training set size and network capacity. Given a large training set, and a net with sufficient capacity to achieve high performance on the training set, nets typically achieved error rates of 4-5% at a 0% reject rate and 1-2% at a 10% reject rate. The topology and capacity of the system, as measured by the number of connections in the net, have surprisingly little effect on generalization. For those developing hand-printed character recognition systems, these results suggest that a large and representative training sample may be the single, most important factor in achieving high recognition accuracy. Benefits of reducing the number of net connections, other than improving generalization, are discussed.