Performance management in competitive distributed Web search

Khoussainov, Rinat; Kushmerick, Nicholas

doi:10.1109/wi.2003.1241259

Cited by 2 publications

(5 citation statements)

References 7 publications

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“…In our simplified version of the problem, we only take into account the cost of resources involved in processing search queries. (Note that we also obtained similar results for a more elaborated cost model that takes into account the cost of document crawling, storage, and maintenance [3].) Under these assumptions, we can use the following formula for search engine performance: P = αQ − βQD, where Q is the number of queries processed in a given time interval, D is the number of documents in the search engine index, α and β are constants.…”

Section: Search Engine Performancementioning

confidence: 58%

Optimising Performance of Competing Search Engines in Heterogeneous Web Environments

Khoussainov

Kushmerick

2003

Machine Learning: ECML 2003

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Abstract. Distributed heterogeneous search environments are an emerging phenomenon in Web search, in which topic-specific search engines provide search services, and metasearchers distribute user's queries to only the most suitable search engines. Previous research has explored the performance of such environments from the user's perspective (e.g., improved quality of search results). We focus instead on performance from the search service provider's point of view (e.g, income from queries processed vs. resources used to answer them). We analyse a scenario in which individual search engines compete for queries by choosing which documents to index. We propose the COUGAR algorithm that specialised search engines can use to decide which documents to index on each particular topic. COUGAR is based on a game-theoretic analysis of heterogeneous search environments, and uses reinforcement learning techniques to exploit the sub-optimal behaviour of its competitors.

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Section: Search Engine Performancementioning

confidence: 58%

Optimising Performance of Competing Search Engines in Heterogeneous Web Environments

Khoussainov

Kushmerick

2003

Machine Learning: ECML 2003

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“…These approaches only discover activities without organizing them into BP or mining their behavioral perspective. According to the number of activities allowed to be detected per message, we divide them into two sub‐categories: The first one regroups the approaches that enable the discovery of a single activity per one message 11‐14 . It consists of assigning one label to each message using supervised, 11 semi‐supervised, 12,13 or unsupervised learning techniques 14 .…”

Section: Overview On Existing Workmentioning

confidence: 99%

“…According to the number of activities allowed to be detected per message, we divide them into two sub‐categories: The first one regroups the approaches that enable the discovery of a single activity per one message 11‐14 . It consists of assigning one label to each message using supervised, 11 semi‐supervised, 12,13 or unsupervised learning techniques 14 . The second sub‐category regroups the approaches that enable the discovery of multiple activities per message 15‐23 …”

Section: Overview On Existing Workmentioning

confidence: 99%

“…Khoussainov and Kushmerick 12,13 proposed an iterative relational learning approach for email task management. This approach exploits the mutual performance improvement between the extraction of speech acts and the identification of related emails.…”

Section: Overview On Existing Workmentioning

confidence: 99%

“…A3: The outcome of the similarity of two messages : This concerns the type of BP elements that can be deduced from the similarity of all the textual content of two messages, especially BP and activities 8,11‐14,32 . For instance, they suppose that if the textual contents of two messages are similar then they belong to the same activity or to the same BP.…”

Section: Qualitative Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Multi‐perspective business process discovery from messaging systems: State‐of‐the art

Elleuch

Laga

Ismaili

et al. 2021

Concurrency and Computation

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At the stage of identifying and modeling business processes (BP), traditional methods for collecting business expertise are time consuming since they mainly rely on physical communication. Moreover, there are some BP with no formal documentation and their execution relies on implicit knowledge of workers. Therefore, analyzing the logs data generated by messaging systems allowing workers to share such implicit knowledge has been a research area of growing interest. BP knowledge introduced by workers in emails could actually cover multiple BP perspectives that would vary from activity, actors, and data perspectives. This article gives an overview of the current state-of-the-art in multi-perspective BP discovery from messaging systems as well as the main existing research questions. Different approaches are presented, discussed, and compared against each other on a qualitative level deduced from the defined set of research questions. The resulting overview covers both advantages and disadvantages of current techniques and the research questions that have not been addressed to date. This should provide a solid basis for further research.

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Performance management in competitive distributed Web search

Cited by 2 publications

References 7 publications

Optimising Performance of Competing Search Engines in Heterogeneous Web Environments

Optimising Performance of Competing Search Engines in Heterogeneous Web Environments

Multi‐perspective business process discovery from messaging systems: State‐of‐the art

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