A Service Class Description (SCD) is an effective meta-data based approach for discovering Deep Web sources whose data exhibit some regular patterns. However, it is tedious and error prone to create an SCD description manually. Moreover, a manually created SCD is not adaptive to the frequent changes of Web sources. It requires its creator to identify all the possible input and output types of a service a priori. In many domains, it is impossible to exhaustively list all the possible input and output data types of a source in advance. In this paper, we describe machine learning approaches for automatic generation of the data types of an SCD. We propose two different approaches for learning data types of a class of Web sources. The Brute-Force Learner is able to generate data types that can achieve high recall, but with low precision. The Clustering-based Learner generates data types that have a high precision rate, but with a lower recall rate. We demonstrate the feasibility of these two learning-based solutions for automatic generation of data types for citation Web sources and presented a quantitative evaluation of these two solutions.

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Lawrence Livermore National Laboratory (LLNL) uses the Cost-Effective Sampling (CES) program for reviewing groundwater data and optimizing the site's groundwater monitoring plan. The CES program produces a data assessment sheet and a lowest-frequency sampling schedule for each groundwater monitoring location. The assessment sheet and recommended sampling schedule greatly streamline the data review process and provide useful information for regulatory and remedial decision-making. The determination of sampling frequency for a given location is based on trend, variability, and magnitude statistics. The underlying principle is that a location's schedule should be determined primarily by the rate of change in concentrations observed there in the recent past. The larger the rate of change, whether upward or downward, the greater the need for frequent sampling. Conversely, where little change is observed, less sampling is recommended. In 1992, CES was approved by the U.S. EPA - Region IX and the local regulators for use at LLNL, and became part of the LLNL's approved compliance monitoring plan (Lamarre et al. 1996). Applying the CES methodology produced, initially, a 40% reduction in the annual number of required groundwater samples, and with recent optimization of the program a 55% reduction has been produced. This reduction saves LLNL $530,000 …

The focus of this review is on trace-element quantitation of microstructures in solids. This review is aimed at the non-specialist who wants to know how SIMS quantitation is achieved. Despite 35 years of SIMS research and applications, SIMS quantitation remains a fundamentally empirical enterprise and is based on standards. The most used standards are ''bulk standards''--solids with a homogeneous distribution of a trace element--and ion-implanted solids. The SIMS systematics of bulk standards and ion-implanted solids are reviewed.

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We investigate the relation between the E spectrum in B {yields} X{sub s}{gamma} decay and the P{sub +} spectrum in semileptonic B {yields} X{sub u}{ell}{bar {nu}} decay (P{sub +} is the hadronic energy minus the absolute value of the hadronic three-momentum), which provides in principle the theoretically simplest determination of |V{sub ub}| from any of the ''shape function regions'' of B {yields} X{sub u}{ell}{bar {nu}} spectra. We calculate analytically the P{sub +} spectrum to order {alpha}{sub s}{sup 2}{beta}{sub 0}, and study its relation to the B {yields} X{sub s}{gamma} photon spectrum to eliminate the leading dependence on nonperturbative effects. We compare the result of fixed order perturbation theory to the next-to-leading log renormalization group improved calculation, and argue that fixed order perturbation theory is likely to be a more appropriate expansion. Implications for the perturbative uncertainties in the determination of |V{sub ub}| from the P{sub +} spectrum are discussed.

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 service discovery scenario and describe our initial experience with DynaBot.