A drawback of existing scientific workflow systems is the lack of support to domain scientists in designing and executing their own scientific workflows. Many domain scientists avoid developing and using workflows because the basic objects of workflows are too low-level and high-level tools and mechanisms to aid in workflow construction and use are largely unavailable. In our research, we are prototyping higher-level abstractions and tools to better support scientists in their workflow activities. Specifically, we are developing generic actors that provide abstract interfaces to specific functionality, workflow templates that encapsulate workflow and data patterns that can be reused and adapted by scientists, and context-awareness mechanisms to gather contextual information from the workflow environment on behalf of the scientist. To evaluate these scientist-centered abstractions on real problems, we apply them to construct and execute scientific workflows in the specific domain area of groundwater modeling and analysis.

Over the past decade, workflows have been successfully applied to a number of scientific domains with great success. Workflow engines are now commonly used across scientific disciplines to automate mundane tasks, collect provenance, and orchestrate complex processes. However, workflows have not yet made significant strides managing fine-grain, concurrent tasks directly on supercomputing platforms. As scientific computing becomes an increasingly important discovery method and high performance computing environments become more complex, addressing this gap becomes critical. Using a simple use case as motivation, this paper describes the current barriers to using workflow engines in a supercomputing environment and outlines the new capabilities that must be provided if workflows are to be successfully applied in this context.

Search engines, such as Google, assign scores to news articles based on their relevancy to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevancy scores do not take into account what makes an article interesting, which would vary from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment there are not enough users that would make collaborative filtering effective. We present a general framework for defining and measuring the "interestingness" of articles, incorporating user-feedback. We show 21% improvement over traditional IR methods

We introduce multiple topic tracking (MTT) for iScore to better recommend news articles for users with multiple interests and to address changes in user interests over time. As an extension of the basic Rocchio algorithm, traditional topic detection and tracking, and single-pass clustering, MTT maintains multiple interest profiles to identify interesting articles for a specific user given user-feedback. Focusing on only interesting topics enables iScore to discard useless profiles to address changes in user interests and to achieve a balance between resource consumption and classification accuracy. Also by relating a topic's interestingness to an article.s interestingness, iScore is able to achieve higher quality results than traditional methods such as the Rocchio algorithm. We identify several operating parameters that work well for MTT. Using the same parameters, we show that MTT alone yields high quality results for recommending interesting articles from several corpora. The inclusion of MTT improves iScore's performance by 9% in recommending news articles from the Yahoo! News RSS feeds and the TREC11 adaptive filter article collection. And through a small user study, we show that iScore can still perform well when only provided with little user feedback.

Conditional random fields (CRFs), which are popular supervised learning models for many natural language processing (NLP) tasks, typically require a large collection of labeled data for training. In practice, however, manual annotation of text documents is quite costly. Furthermore, even large labeled training sets can have arbitrarily limited performance peaks if they are not chosen with care. This paper considers the use of multi-criterion active learning for identification of a small but sufficient set of text samples for training CRFs. Our empirical results demonstrate that our method is capable of reducing the manual annotation costs, while also limiting the retraining costs that are often associated with active learning. In addition, we show that the generalization performance of CRFs can be enhanced through judicious selection of training examples

This paper presents DynaBot, a domain-specific Web service discovery system. The core idea of the DynaBot service discovery system is to use domain-specific service class descriptions powered by an intelligent Deep Web crawler. In contrast to current registry-based service discovery systems -like the several available UDDI registries - DynaBot promotes focused crawling of the Deep Web of services and discovers candidate services that are relevant to the domain of interest. It uses intelligent filtering algorithms to match services found by focused crawling with the domain-specific service class descriptions. We demonstrate the capability of DynaBot through the BLAST scenario and describe our initial experience with DynaBot.

Modern Bioinformatics data sources are widely used by molecular biologists for homology searching and new drug discovery. User-friendly and yet responsive access is one of the most desirable properties for integrated access to the rapidly growing, heterogeneous, and distributed collection of data sources. The increasing volume and diversity of digital information related to bioinformatics (such as genomes, protein sequences, protein structures, etc.) have led to a growing problem that conventional data management systems do not have, namely finding which information sources out of many candidate choices are the most relevant and most accessible to answer a given user query. We refer to this problem as the query routing problem. In this paper we introduce the notation and issues of query routing, and present a practical solution for designing a scalable query routing system based on multi-level progressive pruning strategies. The key idea is to create and maintain source capability profiles independently, and to provide algorithms that can dynamically discover relevant information sources for a given query through the smart use of source profiles. Compared to the keyword-based indexing techniques adopted in most of the search engines and software, our approach offers fine-granularity of interest matching, thus it is more powerful and effective for handling queries with complex conditions.

To provide scientists and engineers with the ability to explore and analyze tera-scale size data-sets we are using a twofold approach. First, we model the data with the objective of creating a compressed yet manageable representation. Second, with that compressed representation, we provide the ability to query the resulting approximation in order to obtain approximate yet sufficient answers; a process called ad-hoc querying. This paper is concerned with a wavelet modeling technique that seeks to capture the important physical characteristics of the target scientific data. Our approach is driven by the compression, which is necessary for viable throughput, along with the end user requirements from the discovery process. Our work contrasts existing research which applies wavelets to range querying, change detection, and clustering problems by working directly with the wavelet decomposition of the data. The difference in this procedure is due primarily to the nature of the data and the requirements of the scientists and engineers. Our approach directly uses the wavelet coefficients of the data to compress as well as query. We describe how the wavelet decomposition is used to facilitate data compression and how queries are posed on the resulting compressed model. Results of this process will be shown for several problems of interest.

We illustrate how a domain scientist can perform a complex scientific task by interleaving data access, querying, and manipulation, as well as analytical steps and computations in complex, problem specific ways. We show how our system is used by a geneticist for solving the problem of discovering the so-called "co-regulated" genes by interlinking data and computation from several Web sites, local computations, as well as local and remote databases. The main distinctive features of our system (compared, e.g., to the ZOO environment (Ioannidis et al., 1996)) include: (i) executable workflows run as Web services; (ii) abstract workflows employ concept names and semantic types that are higher-level (and thus more "scientist friendly") than executable workflows; and (iii) our system supports automatic translation of the latter into the former.

As scientific data sets grow exponentially in size, the need for scalable algorithms that heuristically partition the data increases. In this paper, we describe the three-step evolution of a hierarchical partitioning algorithm for large-scale spatio-temporal scientific data sets generated by massive simulations. The first version of our algorithm uses a simple top-down partitioning technique, which divides the data by using a four-way bisection of the spatio-temporal space. The shortcomings of this algorithm lead to the second version of our partitioning algorithm, which uses a bottom-up approach. In this version, a partition hierarchy is constructed by systematically agglomerating the underlying Cartesian grid that is placed on the data. Finally, the third version of our algorithm utilizes the intrinsic topology of the data given in the original scientific problem to build the partition hierarchy in a bottom-up fashion. Specifically, the topology is used to heuristically agglomerate the data at each level of the partition hierarchy. Despite the growing complexity in our algorithms, the third version of our algorithm builds partition hierarchies in less time and is able to build trees for larger size data sets as compared to the previous two versions.

MOTIVATION: The World Wide Web provides an incredible resource to genomics researchers in the form of query access to distributed data sources--e.g. BLAST sequence homology search interfaces. The number of these autonomous sources and their rate of change outpaces the speed at which they can be manually classified, meaning that the available data is not being utilized to its full potential. Manually maintaining a wrapper library will not scale to accommodate the growth of genomics data sources on the Web, challenging us to produce an automated system that can find, classify and wrap new sources without constant human intervention. Previous research has not addressed the problem of automatically locating, classifying and integrating classes of bioinformatics data sources. RESULTS: This paper presents an overview of a system for finding classes of bioinformatics data sources and integrating them behind a unified interface. We describe our approach for automatic classification of new Web sources into relevance categories that eliminates the human effort required to maintain a current repository of sources. Our approach is based on a meta-data description of classes of interesting sources that describes the important features of an entire class of services without tying that description to any particular Web source. We examine the features of this format in the context of BLAST sources to show how it relates to Web sources that are being described. We then show how a description can be used to determine if an arbitrary Web source is an instance of the described service. To validate the effectiveness of this approach, we have constructed a prototype that correctly classifies approximately two-thirds of the BLAST sources we tested. We conclude with a discussion of these results, the factors that affect correct automatic classification and areas for future study.

With the advent of fast computer systems, scientists are now able to generate terabytes of simulation data. Unfortunately, the sheer size of these data sets has made efficient exploration of them impossible. To aid scientists in gleaning insight from their simulation data, we have developed an ad-hoc query infrastructure. Our system, called AQSim (short for Ad-hoc Queries for Simulation) reduces the data storage requirements and query access times in two stages. First, it creates and stores mathematical and statistical models of the data at multiple resolutions. Second, it evaluates queries on the models of the data instead of on the entire data set. In this paper, we present two simple but effective statistical modeling techniques for simulation data. Our first modeling technique computes the "true" (unbiased) mean of systematic partitions of the data. It makes no assumptions about the distribution of the data and uses a variant of the root mean square error to evaluate a model. Our second statistical modeling technique uses the Andersen-Darling goodness-of-fit method on systematic partitions of the data. This method evaluates a model by how well it passes the normality test on the data. Both of our statistical models effectively answer range queries. At each resolution of the data, we compute the precision of our answer to the user's query by scaling the one-sided Chebyshev Inequalities with the original mesh's topology. We combine precisions at different resolutions by calculating their weighted average. Our experimental evaluations on two scientific simulation data sets illustrate the value of using these statistical modeling techniques on multiple resolutions of large simulation data sets.

Data warehouses and data marts have been successfully applied to a multitude of commercial business applications. They have proven to be invaluable tools by integrating information from distributed, heterogeneous sources and summarizing this data for use throughout the enterprise. Although the need for information dissemination is as vital in science as in business, working warehouses in this community are scarce because traditional warehousing techniques do not transfer to scientific environments. There are two primary reasons for this difficulty. First, schema integration is more difficult for scientific databases than for business sources, because of the complexity of the concepts and the associated relationships. While this difference has not yet been fully explored, it is an important consideration when determining how to integrate autonomous sources. Second, scientific data sources have highly dynamic data representations (schemata). When a data source participating in a warehouse changes its schema, both the mediator transferring data to the warehouse and the warehouse itself need to be updated to reflect these modifications. The cost of repeatedly performing these updates in a traditional warehouse, as is required in a dynamic environment, is prohibitive. This paper discusses these issues within the context of the DataFoundry project, an ongoing research effort at Lawrence Livermore National Laboratory. DataFoundry utilizes a unique integration strategy to identify corresponding instances while maintaining differences between data from different sources, and a novel architecture and an extensive meta-data infrastructure, which reduce the cost of maintaining a warehouse.

Mediators are a critical component of any data warehouse; they transform data from source formats to the warehouse representation while resolving semantic and syntactic conflicts. The close relationship between mediators and databases requires a mediator to be updated whenever an associated schema is modified. Failure to quickly perform these updates significantly reduces the reliability of the warehouse because queries do not have access to the most current data. This may result in incorrect or misleading responses, and reduce user confidence in the warehouse. Unfortunately, this maintenance may be a significant undertaking if a warehouse integrates several dynamic data sources. This paper describes a meta-data framework, and associated software, designed to automate a significant portion of the mediator generation task and thereby reduce the effort involved in adapting to schema changes. By allowing the DBA to concentrate on identifying the modifications at a high level, instead of reprogramming the mediator, turnaround time is reduced and warehouse reliability is improved.

The growing maturity of ODBMS technology is causing many enterprises to consider migrating relational databases to ODBIMS's. While data remapping is relatively straight-forward in most cases, greater challenges lie in economically and non-invasively adapting legacy application software. We report on a genetics laboratory database migration experiment, which was facilitated by both organization of the relational data in object-like form and a C++-framework designed to insulate application code from relational artifacts. Although this experiment was largely successful, we discovered to our surprise that the framework failed to encapsulate three subtle aspects of the relational implementation, thereby ''contaminating'' application code. We analyze the underlying issues, and offer cautionary guidance to future migrators.

The Human Genome Project poses severe challenges in database design and implementation. These include comprehensive coverage of diverse data domains and user constituencies; robustness in the presence of incomplete, inconsistent and multi-version data; accessibility through many levels of abstraction, and scalability in content and organizational complexity. The paper presents a new data model developed to meet these challenges by the Utah Center for Human Genome Research. The central characteristics are: (i) a high level data model comprising five broadly applicable workflow notions; (ii) representation of those notions as objects in an extended relational model; (iii) expression of working database schemas as meta data in administration tables; (iv) population of the database through tables dependent on the meta data tables; and (v) implementation via a conventional relational database management system. The authors explore two advantages of this approach: the resulting representational flexibility, and the reflective use of meta data to accomplish schema evolution by ordinary updates. Implementation and performance pragmatics of this work are sketched, as well as implications for future database development.