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- 304 creator boualem-benatallah.
- 304 creator guangyuan-xu.
- 304 creator hamid-motahari.
- 304 type InProceedings.
- 304 label "Protocol-Aware Matching of Web Service Interfaces for Adapter Development".
- 304 sameAs 304.
- 304 abstract "With the rapid growth in the number of online Web services, the problem of service adaptation has received significant attention. In matching and adaptation, the functional description of services including interface and data as well as behavioral descriptions are important. Existing work on matching and adaptation focuses either on service interfaces or behavioral descriptions. In this paper, we present a semi-automated matching approach that considers both service descriptions: we introduce two protocol-aware service interface matching algorithms, i.e. depth-based interface matching and iterative reference-based interface matching. These algorithms refine the results of interface matching by incorporating the ordering constraints imposed by business protocol definitions on service operations. We have implemented a prototype and performed experiments using the specification of synthetic and real-world Web services. Experiments show that the proposed approaches lead to a significant improvement in the quality of matching between services.".
- 304 hasAuthorList authorList.
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- 304 keyword "Tools for analyzing".
- 304 keyword "composing".
- 304 keyword "designing".
- 304 keyword "discovering Web services".
- 304 keyword "publishing".
- 304 title "Protocol-Aware Matching of Web Service Interfaces for Adapter Development".
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- item-1 hasContent hamid-motahari.
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- 313 creator jure-leskovec.
- 313 creator kevin-lang.
- 313 creator michael-mahoney.
- 313 type InProceedings.
- 313 label "Empirical Comparison of Algorithms for Network Community Detection".
- 313 sameAs 313.
- 313 abstract "Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that ``look like'' good communities for the application of interest. In this paper, we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases of clusters they identify. We evaluate several common objective functions that are used to formalize the notion of a network community, and examine several different classes of approximation algorithms that aim to optimize such objective functions. In addition, rather than simply fixing an objective and asking for an approximation to the best cluster of any size, we consider a size-resolved version of the optimization problem. Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior.".
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- 313 keyword "Community discovery".
- 313 keyword "social media analysis".
- 313 keyword "social network analysis".
- 313 keyword "structure modeling".
- 313 title "Empirical Comparison of Algorithms for Network Community Detection".
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- 317 creator josh-attenberg.
- 317 creator shuai-ding.
- 317 creator torsten-suel.
- 317 type InProceedings.
- 317 label "Scalable Techniques for Document Identifier Assignment in Inverted Indexes".
- 317 sameAs 317.
- 317 abstract "Web search engines are based on a full-text data structure called an inverted index. The size of the inverted index is a major performance bottleneck during query processing, and a large amount of research has focused on fast and effective techniques for compressing this structure.Several authors have recently proposed techniques for improving index compression by optimizing the assignment of document identifiers to the documents in the collection, leading to significant improvements in overall index size. In this paper, we propose improved techniques for document identifier assignment. Previous work includes simple and fast heuristics such as sorting by URL, as well as more involved approaches based on Travelling Salesman or graph partitioning problems that achieve good compression but do not scale to larger document collections. We propose a new framework based on performing a Travelling Salesman computation on a reduced sparse graph obtained using Locally Sensitive Hashing, which achieves improved compression while scaling to tens of millions of documents. Based on this framework, we describe a number of new algorithms, and perform a detailed evaluation on three large data sets showing improvements in index size.".
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- 317 keyword "Indexing".