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- 582 title "On Anonymizing Query Logs via Token-based Hashing".
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- item-1 hasContent ravi-kumar.
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- item-2 hasContent jasmine-novak.
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- item-3 hasContent bo-pang.
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- item-4 hasContent andrew-tomkins.
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- 584 creator axel-marten.
- 584 creator boualem-benatallah.
- 584 creator fabio-casati.
- 584 creator francisco-curbera.
- 584 creator hamid-motahari.
- 584 type InProceedings.
- 584 label "Semi-Austomated Adaptation of Service Interactions".
- 584 sameAs 584.
- 584 abstract "In today's Web, many functionality-wise similar Web services are offered through heterogeneous interfaces (operation definitions) and business protocols (ordering constraints defined on legal operation invocation sequences). The typical approach to enable interoperation in such a heterogeneous setting is through developing adapters. There have been approaches for classifying possible mismatches between service interfaces and business protocols to facilitate adapter development. However, the hard job is that of identifying, given two service specifications, the actual mismatches between their interfaces and business protocols.<br /><br /><br /><br /> In this paper we present novel techniques and a tool that provides semi-automated support for identifying and resolution of mismatches between service interfaces and protocols, and for generating adapter specification. We make the following main contributions: (i) we identify mismatches between service interfaces, which leads to finding mismatches of type of signature, merge/split, and extra/missing messages; (ii) we identify all ordering mismatches between service protocols and generate a tree, called mismatch tree, for mismatches that require developers' input for their resolution. In addition, we provide semi-automated support in analyzing the mismatch tree to help in resolving such mismatches. We have implemented the approach in a tool inside IBM WID (WebSphere Integration Developer). Our experiments with some real-world case studies show the viability of the proposed approach. The methods and tool are significant in that they considerably simplify the problem of adapting services so that interoperation is possible.".
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- 584 title "Semi-Austomated Adaptation of Service Interactions".
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- item-1 hasContent hamid-motahari.
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- item-2 hasContent boualem-benatallah.
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- item-3 hasContent axel-marten.
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- item-4 hasContent francisco-curbera.
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- item-5 hasContent fabio-casati.
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- 586 creator anthony-tomasic.
- 586 creator isaac-simmons.
- 586 creator john-zimmerman.
- 586 type InProceedings.
- 586 label "Learning Information Intent via Observation".
- 586 sameAs 586.
- 586 abstract "Users in an organization frequently request help by sending request messages to assistants that express an information intent: an intention to update an information system. Assistants spend a significant amount of time and effort processing these messages. For example, human resource assistants process requests to update personnel records, and executive assistants process requests to schedule conference rooms or to make travel reservations. To process the intent of a message, assistants read the message and then locate, complete, and submit a form that corresponds to the expressed intent . Automatically or semi-automatically processing the intent of a message on behalf of an assistant would ease the mundane and repetitive nature of this kind of work. For a well-understood domain, a straightforward application of natural language processing techniques can be used to build an intelligent form interface to semi-automatically process information intents. However, high performance parsers are based on machine learning algorithms that require a large collection of messages that have been labeled by an expert. The generation of a labeled corpus of messages is a major barrier to the construction of a parser. In this paper, we investigate the construction of a natural language processing system and an intelligent form system that observes an assistant processing a request. The intelligent form system then generates a labeled training corpus by interpreting the observations. This paper reports on the measurement of the performance of the machine learning algorithms based on real data. The combination of observations, machine learning and interaction design produces an effective intelligent form interface based on natural language processing.".
- 586 hasAuthorList authorList.
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- 586 title "Learning Information Intent via Observation".
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- item-1 hasContent anthony-tomasic.
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- item-2 hasContent isaac-simmons.
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- item-3 hasContent john-zimmerman.
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- 588 creator deepayan-chakrabarti.
- 588 creator kunal-punera.
- 588 creator ravi-kumar.
- 588 type InProceedings.
- 588 label "Page-level Template Detection via Isotonic Smoothing".
- 588 sameAs 588.
- 588 abstract "We develop a novel framework for the ``page-level'' template detection problem. Our framework is built on two main ideas. The first is the automatic generation of training data for a classifier that, given a page, assigns a templateness score to every DOM node of the page. The second is the global smoothing of these per-node classifier scores by solving a regularized isotonic regression problem; the latter follows from a simple yet powerful abstraction of templateness on a page. Our extensive experiments on human-labeled test data show that our approach detects templates effectively.".
- 588 hasAuthorList authorList.
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- 588 title "Page-level Template Detection via Isotonic Smoothing".
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