Continuous time-series sequence matching, specifically, matching a numeric live stream against a set of redefined pattern sequences, is critical for domains ranging from fire spread tracking to network traffic monitoring. While several algorithms exist for similarity matching of static time-series data, matching continuous data poses new, largely unsolved challenges including online real-time processing requirements and system resource limitations for handling infinite streams. In this work, we propose a novel live stream matching framework, called n-Snippet Indices Framework (in short, SNIF), to tackle these challenges. SNIF employs snippets as the basic unit for matching streaming time-series. The insight is to perform the matching at two levels of granularity: bag matching of subsets of snippets of the live stream against prefixes of the patterns, and order checking for maintaining successive candidate snippet bag matches. We design a two-level index structure, called SNIF index, which supports these two modes of matching. We propose a family of online two-level prefix matching algorithms that trade off between result accuracy and response time. The effectiveness of SNIF to detect patterns has been thoroughly tested through experiments using real datasets from the domains of fire monitoring and sensor motes. In this paper, we also present a study of SNIF's performance, accuracy and tolerance to noise compared against those of the state-of-the-art Continuous Query with Prediction (CQP) approach.

A completed software product is the end result of many decisions that must be made throughout the development lifecycle. Unfortunately, the rationale for these decisions is usually not captured and is therefore lost. The Software Using RATionale (SEURAT) system integrates with the Eclipse Interactive Development Environment to support rationale capture and use. In addition to presenting the rationale to the developer/maintainer as needed, SEURAT also supports requirements traceability and impact assessment.

In scientific domains, knowledge is often discovered from experiments by grouping or clustering them based on the similarity of their output. The causes of similarity are analyzed based on the input conditions characterizing a given type of output, i.e., a given cluster. This analysis helps in applications such as decision support in industry. Cluster representatives form at-a-glance depictions for such applications. Randomly selecting a set of conditions in a cluster as its representative is not sufficient since distinct combinations of inputs could lead to the same cluster. In this paper, an approach called DesCond is proposed to design semantics-preserving cluster representatives for scientific input conditions. We define a notion of distance for conditions to capture semantics based on the types of their attributes and their relative importance. Using this distance, methods of building candidate cluster representatives with different levels of detail are proposed. Candidates are compared using the DesCond Encoding proposed in this paper that assesses their complexity and information loss, given user interests. The candidate with the lowest encoding for each cluster is returned as its designed representative. DesCond is evaluated with real data from Materials Science. Evaluation with domain expert interviews and formal user surveys shows that designed representatives consistently outperform randomly selected ones and different candidates suit different users.

For a number of years, members of the artificial intelligence (AI) in design community have studied design rationale (DR), the reasons behind decisions made while designing. A record of what decisions were made, and why, is especially valuable for software maintenance. One reason for this is that the software lifecycle is a long one. Large projects may take years to complete and spend even more time out in the field being used (and maintained). The combination of a long lifecycle and the typically high personnel turnover in the software industry increases the probability that the original designer is unlikely to be available for consultation when problems arise. J. Lee's survey (1997) presents an excellent overview of DR research. There has also been work specific to software design, such as Boehm's WinWin (1994).

This paper describes our experiences with the Webware, Interfaces and Networking Experimental (WINE) Laboratory. The WINE Lab was created to assist in teaching the topics of computer networks, user interfaces and webware. The goal of the lab is to provide students with the opportunity to complete projects, experiment with relevant techniques and make connections between topics with resources not available in a general purpose Unix-based computing environment. The results from offering courses with the lab show success in meeting these goals. 1 Introduction The paper describes our experiences with the Webware, Interfaces and Networking Experimental Laboratory (WINE Lab) here at Worcester Polytechnic Institute (WPI). This lab was developed to assist in teaching the topics of computer networks, user interfaces and webware. The objective in developing the WINE Lab was to improve these three courses by providing students with the opportunity to complete projects, experiment with relevant...