Greg Barish

Ariadne: a system for constructing mediators for Internet sources

The Web is based on a browsing paradigm that makes it difficult to retrieve and integrate data from multiple sites. Today, the only way to achieve this integration is by building specialized applications, which are time-consuming to develop and difficult to maintain. We are addressing this problem by creating the technology and tools for rapidly constructing information mediators that extract, query, and integrate data from web sources. The resulting system, called Ariadne, makes it feasible to rapidly build information mediators that access existing web sources.

Speculative plan execution for information gathering

The execution performance of an information gathering plan can suffer significantly due to remote I/O latencies. A streaming dataflow model of execution addresses the problem to some extent, exploiting all natural opportunities for parallel execution, as allowed by the data dependencies in a plan. Unfortunately, plans that integrate information from multiple sources often use the results of one operation as the basis for forming queries to a subsequent operation. Such cases require sequential execution, an inefficiency that can erase prior gains made through techniques like streaming dataflow. To address this problem, we present a technique called speculative plan execution, an out-of-order method that capitalizes on knowledge gained from prior executions as a means for overcoming remaining data dependencies between plan operators. Our approach inserts additional plan operators that generate and confirm speculative results, while preserving the safety and fairness of overall execution. To increase the utility of speculative execution, we propose a method of value prediction that combines caching with the more effective and space-efficient techniques of classification and transduction. We present experimental results that demonstrate how the performance of information gathering plans can benefit from speculative execution and how its overall utility can be increased through our hybrid method of value prediction.

Alternative techniques for the efficient acquisition of haptic data

Immersive environments are those that surround users in an artificial world. These environments consist of a composition of various types of immersidata: unique data types that are combined to render a virtual experience. Acquisition, for storage and future querying, of information describing sessions in these environments is challenging because of the real-time demands and sizeable amounts of data to be managed. In this paper, we summarize a comparison of techniques for achieving the efficient acquisition of one type of immersidata, the haptic data type, which describes the movement, rotation, and force associated with user-directed objects in an immersive environment. In addition to describing a general process for real-time sampling and recording of this type of data, we propose three distinct sampling strategies: fixed, grouped, and adaptive. We conducted several experiments with a real haptic device and found that there are tradeoffs between the accuracy, efficiency, and complexity of implementation for each of the proposed techniques. While it is possible to use any of these approaches for real-time haptic data acquisition, we found that an adaptive sampling strategy provided the most efficiency without significant loss in accuracy. As immersive environments become more complex and contain more haptic sensors, techniques such as adaptive sampling can be useful for improving scalability of real-time data acquisition.

An expressive language and efficient execution system for software agents

Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information -- typically, a slow, I/O-bound process -- it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution system that enable the efficient execution of expressive agent plans. The plan language allows complex tasks to be more easily expressed by providing a variety of operators for flexibly processing the data as well as supporting subplans (for modularity) and recursion (for indeterminate looping). The executor is based on a streaming dataflow model of execution to maximize the amount of operator and data parallelism possible at runtime. We have implemented both the language and executor in a system called THESEUS. Our results from testing THESEUS show that streaming dataflow execution can yield significant speedups over both traditional serial (von Neumann) as well as nonstreaming dataflow-style execution that existing software and robot agent execution systems currently support. In addition, we show how plans written in the language we present can represent certain types of subtasks that cannot be accomplished using the languages supported by network query engines. Finally, we demonstrate that the increased expressivity of our plan language does not hamper performance; specifically, we show how data can be integrated from multiple remote sources just as efficiently using our architecture as is possible with a state-of-the-art streaming-dataflow network query engine.

Dataflow plan execution for software agents

Recent research has made it possible to build information agents that retrieve and integrate information from the World Wide Web. Although there now exist solutions for modeling Web sources, query planning, and information extraction, less attention has been given to the problem of optimizing agent execution. In this paper, we describe Theseus, an efficient agent plan execution system. Through its pipelined, dataflow-based architecture, Theseus offers a high degree of parallelism and asynchronous information routing, features that can substantially improve performance. Theseus differs from prior work in reactive planning systems and parallel databases because it gathers information from the Web, a domain where information retrieval is a problem that is network-bound and is often based on interleaved data gathering and navigation. The Theseus plan language and architecture directly address these issues, resulting in a highperformance execution system.

An efficient plan execution system for information management agents

Recent work on information integration has yielded novel and efficient solutions for gathering data from the World Wide Web. However, there has been little attention given to the problem of providing information management capabilities that closely model how people interact with the web in productive ways - not only collecting information, but monitoring web sites for new or updated data, sending notifications based on the results, building reports, creating local repositories of information, and so on. These needs are unique to the dynamic nature of information in a networked environment. In this paper, we describe Theseus, an efficient plan execution system for information management agents. Through its plan language, Theseus supports a number of capabilities which enable practical information management, including repeated and periodic query execution, conditional plan declarations, query result aggregation, and flexible communication of results. The Theseus executor system focuses on efficiency, with support for data pipelining, and dataflow-based, event driven parallel execution. With Theseus, users can automate the complex but practical ways in which they interact with the web, for both information gathering and management.