46726656
OBL: a formal deduction method for object-oriented systems
Object Behavior Logic (OBL) is proposed in order to specify object oriented system behavior. An inference engine is introduced for verifying the consistency of the systems based on the specification in OBL. OBL uses the paradigms of state machine to catch the dynamic aspect of object oriented systems in formal specification, plus the features of object oriented concepts such as inheritance, instantiation, and so forth, which separate the unique object oriented techniques from the function oriented techniques.
17374517
Model Checking with Induction
We have proposed inductive model checking (IMC) to alleviate the state explosion problem in model checking. IMC divides the whole verification tasks into several sub-tasks, and applies the three inductive steps (basis, hypothesis, and induction) to verify a system with model checking. In this paper, we continue our research by determining those temporal formulas, namely, persistent temporal formulas, which are verifiable with IMC. One important characteristic of a persistent temporal formula is that it contains a periodic temporal formula that must be held persistently. From the verification results of the periodic formula with different sequences of states, IMC concludes the verification of the persistent temporal formula
15025719
Entity Search Engine: Towards Agile Best-Effort Information Integration over the Web
15818010
Automatic Partitioner for Behavior Level Distributed Logic Simulation
As the complexity of circuit design increases, verification through simulation has become a bottleneck of the IC design process. Distributed parallel simulation is one way to solving the problem. In order to distribute the simulation workload to multiple processors, the design must be carefully partitioned first. While most previous work focus on gate level partitioning, our work extends a previously implemented Verilog gate-level partitioner to support RTL and behavior level partitioning. Techniques to partition special constructs specific to these levels, such as global access, function calls and memory access, are described in this paper. The experimental results show that our techniques are capable of finding partitions which can accelerate simulation.
14846368
Progressive and selective merge: computing top-k with ad-hoc ranking functions
The family of threshold algorithm (ie, TA) has been widely studied for efficiently computing top-k queries. TA uses a sort-merge framework that assumes data lists are pre-sorted, and the ranking functions are monotone. However, in many database applications, attribute values are indexed by tree-structured indices (eg, B-tree, R-tree), and the ranking functions are not necessarily monotone. To answer top-k queries with ad-hoc ranking functions, this paper studies anindex-merge paradigm that performs progressive search over the space of joint states composed by multiple index nodes.
 We address two challenges for efficient query processing. First, to minimize the search complexity, we present a double-heap algorithm which supports not only progressive state search but also progressive state generation. Second, to avoid unnecessary disk access, we characterize a type of "empty-state" that does not contribute to the final results, and propose a new materialization model, join-signature, to prune empty-states. Our performance study shows that the proposed method achieves one order of magnitude speed-up over baseline solutions.
4658514
Structured databases on the web: observations and implications
The Web has been rapidly "deepened" by the prevalence of databases online. With the potentially unlimited information hidden behind their query interfaces, this "deep Web" of searchable databses is clearly an important frontier for data access. This paper surveys this relatively unexplored frontier, measuring characteristics pertinent to both exploring and integrating structured Web sources. On one hand, our "macro" study surveys the deep Web at large, in April 2004, adopting the random IP-sampling approach, with one million samples. (How large is the deep Web? How is it covered by current directory services?) On the other hand, our "micro" study surveys source-specific characteristics over 441 sources in eight representative domains, in December 2002. (How "hidden" are deep-Web sources? How do search engines cover their data? How complex and expressive are query forms?) We report our observations and publish the resulting datasets to the research community. We conclude with several implications (of our own) which, while necessarily subjective, might help shape research directions and solutions.
6980873
Making holistic schema matching robust: an ensemble approach
The Web has been rapidly "deepened" by myriad searchable databases online, where data are hidden behind query interfaces. As an essential task toward integrating these massive "deep Web" sources, large scale schema matching (i.e., discovering semantic correspondences of attributes across many query interfaces) has been actively studied recently. In particular, many works have emerged to address this problem by "holistically" matching many schemas at the same time and thus pursuing "mining" approaches in nature. However, while holistic schema matching has built its promise upon the large quantity of input schemas, it also suffers the robustness problem caused by noisy data quality. Such noises often inevitably arise in the automatic extraction of schema data, which is mandatory in large scale integration. For holistic matching to be viable, it is thus essential to make it robust against noisy schemas. To tackle this challenge, we propose a data-ensemble framework with sampling and voting techniques, which is inspired by bagging predictors. Specifically, our approach creates an ensemble of matchers, by randomizing input schema data into many independently downsampled trials, executing the same matcher on each trial and then aggregating their ranked results by taking majority voting. As a principled basis, we provide analytic justification of the effectiveness of this data-ensemble framework. Further, empirically, our experiments on real Web data show that the "ensemblization" indeed significantly boosts the matching accuracy under noisy schema input, and thus maintains the desired robustness of a holistic matcher.