The Experts below are selected from a list of 13773 Experts worldwide ranked by ideXlab platform

Benny Kimelfeld - One of the best experts on this subject based on the ideXlab platform.

  • automatic suggestion of query rewrite rules for Enterprise Search
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012
    Co-Authors: Zhuowei Bao, Benny Kimelfeld
    Abstract:

    Enterprise Search is challenging for several reasons, notably the dynamic terminology and jargon that are specific to the Enterprise domain. This challenge is partly addressed by having domain experts maintaining the Enterprise Search engine and adapting it to the domain specifics. Those administrators commonly address user complaints about relevant documents missing from the top matches. For that, it has been proposed to allow administrators to influence Search results by crafting query-rewrite rules, each specifying how queries of a certain pattern should be modified or augmented with additional queries. Upon a complaint, the administrator seeks a semantically coherent rule that is capable of pushing the desired documents up to the top matches. However, the creation and maintenance of rewrite rules is highly tedious and time consuming. Our goal in this work is to ease the burden on Search administrators by automatically suggesting rewrite rules. This automation entails several challenges. One major challenge is to select, among many options, rules that are ``natural'' from a semantic perspective (e.g., corresponding to closely related and syntactically complete concepts). Towards that, we study a machine-learning classification approach. The second challenge is to accommodate the cross-query effect of rules---a rule introduced in the context of one query can eliminate the desired results for other queries and the desired effects of other rules. We present a formalization of this challenge as a generic computational problem. As we show that this problem is highly intractable in terms of complexity theory, we present heuristic approaches and optimization thereof. In an experimental study within IBM intranet Search, those heuristics achieve near-optimal quality and well scale to large data sets.

  • SIGIR - Automatic suggestion of query-rewrite rules for Enterprise Search
    Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12, 2012
    Co-Authors: Zhuowei Bao, Benny Kimelfeld
    Abstract:

    Enterprise Search is challenging for several reasons, notably the dynamic terminology and jargon that are specific to the Enterprise domain. This challenge is partly addressed by having domain experts maintaining the Enterprise Search engine and adapting it to the domain specifics. Those administrators commonly address user complaints about relevant documents missing from the top matches. For that, it has been proposed to allow administrators to influence Search results by crafting query-rewrite rules, each specifying how queries of a certain pattern should be modified or augmented with additional queries. Upon a complaint, the administrator seeks a semantically coherent rule that is capable of pushing the desired documents up to the top matches. However, the creation and maintenance of rewrite rules is highly tedious and time consuming. Our goal in this work is to ease the burden on Search administrators by automatically suggesting rewrite rules. This automation entails several challenges. One major challenge is to select, among many options, rules that are ``natural'' from a semantic perspective (e.g., corresponding to closely related and syntactically complete concepts). Towards that, we study a machine-learning classification approach. The second challenge is to accommodate the cross-query effect of rules---a rule introduced in the context of one query can eliminate the desired results for other queries and the desired effects of other rules. We present a formalization of this challenge as a generic computational problem. As we show that this problem is highly intractable in terms of complexity theory, we present heuristic approaches and optimization thereof. In an experimental study within IBM intranet Search, those heuristics achieve near-optimal quality and well scale to large data sets.

Zhuowei Bao - One of the best experts on this subject based on the ideXlab platform.

  • automatic suggestion of query rewrite rules for Enterprise Search
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2012
    Co-Authors: Zhuowei Bao, Benny Kimelfeld
    Abstract:

    Enterprise Search is challenging for several reasons, notably the dynamic terminology and jargon that are specific to the Enterprise domain. This challenge is partly addressed by having domain experts maintaining the Enterprise Search engine and adapting it to the domain specifics. Those administrators commonly address user complaints about relevant documents missing from the top matches. For that, it has been proposed to allow administrators to influence Search results by crafting query-rewrite rules, each specifying how queries of a certain pattern should be modified or augmented with additional queries. Upon a complaint, the administrator seeks a semantically coherent rule that is capable of pushing the desired documents up to the top matches. However, the creation and maintenance of rewrite rules is highly tedious and time consuming. Our goal in this work is to ease the burden on Search administrators by automatically suggesting rewrite rules. This automation entails several challenges. One major challenge is to select, among many options, rules that are ``natural'' from a semantic perspective (e.g., corresponding to closely related and syntactically complete concepts). Towards that, we study a machine-learning classification approach. The second challenge is to accommodate the cross-query effect of rules---a rule introduced in the context of one query can eliminate the desired results for other queries and the desired effects of other rules. We present a formalization of this challenge as a generic computational problem. As we show that this problem is highly intractable in terms of complexity theory, we present heuristic approaches and optimization thereof. In an experimental study within IBM intranet Search, those heuristics achieve near-optimal quality and well scale to large data sets.

  • SIGIR - Automatic suggestion of query-rewrite rules for Enterprise Search
    Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval - SIGIR '12, 2012
    Co-Authors: Zhuowei Bao, Benny Kimelfeld
    Abstract:

    Enterprise Search is challenging for several reasons, notably the dynamic terminology and jargon that are specific to the Enterprise domain. This challenge is partly addressed by having domain experts maintaining the Enterprise Search engine and adapting it to the domain specifics. Those administrators commonly address user complaints about relevant documents missing from the top matches. For that, it has been proposed to allow administrators to influence Search results by crafting query-rewrite rules, each specifying how queries of a certain pattern should be modified or augmented with additional queries. Upon a complaint, the administrator seeks a semantically coherent rule that is capable of pushing the desired documents up to the top matches. However, the creation and maintenance of rewrite rules is highly tedious and time consuming. Our goal in this work is to ease the burden on Search administrators by automatically suggesting rewrite rules. This automation entails several challenges. One major challenge is to select, among many options, rules that are ``natural'' from a semantic perspective (e.g., corresponding to closely related and syntactically complete concepts). Towards that, we study a machine-learning classification approach. The second challenge is to accommodate the cross-query effect of rules---a rule introduced in the context of one query can eliminate the desired results for other queries and the desired effects of other rules. We present a formalization of this challenge as a generic computational problem. As we show that this problem is highly intractable in terms of complexity theory, we present heuristic approaches and optimization thereof. In an experimental study within IBM intranet Search, those heuristics achieve near-optimal quality and well scale to large data sets.

Elaine G Toms - One of the best experts on this subject based on the ideXlab platform.

  • Enterprise Search behaviour of software engineers
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006
    Co-Authors: Luanne Freund, Elaine G Toms
    Abstract:

    Technical professionals spend ~25% of their time at work Searching for information, and have specialized information needs that are not well-served by generic Enterprise Search tools. In this study, we investigated how a group of software engineers use a workplace Search system. We identify patterns of Search behaviour specific to this group and distinct from general web and intranet Search patterns, and make design recommendations for Search systems that will better serve the needs of this group.

  • SIGIR - Enterprise Search behaviour of software engineers
    Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06, 2006
    Co-Authors: Luanne Freund, Elaine G Toms
    Abstract:

    Technical professionals spend ~25% of their time at work Searching for information, and have specialized information needs that are not well-served by generic Enterprise Search tools. In this study, we investigated how a group of software engineers use a workplace Search system. We identify patterns of Search behaviour specific to this group and distinct from general web and intranet Search patterns, and make design recommendations for Search systems that will better serve the needs of this group.

Luanne Freund - One of the best experts on this subject based on the ideXlab platform.

  • Enterprise Search behaviour of software engineers
    International ACM SIGIR Conference on Research and Development in Information Retrieval, 2006
    Co-Authors: Luanne Freund, Elaine G Toms
    Abstract:

    Technical professionals spend ~25% of their time at work Searching for information, and have specialized information needs that are not well-served by generic Enterprise Search tools. In this study, we investigated how a group of software engineers use a workplace Search system. We identify patterns of Search behaviour specific to this group and distinct from general web and intranet Search patterns, and make design recommendations for Search systems that will better serve the needs of this group.

  • SIGIR - Enterprise Search behaviour of software engineers
    Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '06, 2006
    Co-Authors: Luanne Freund, Elaine G Toms
    Abstract:

    Technical professionals spend ~25% of their time at work Searching for information, and have specialized information needs that are not well-served by generic Enterprise Search tools. In this study, we investigated how a group of software engineers use a workplace Search system. We identify patterns of Search behaviour specific to this group and distinct from general web and intranet Search patterns, and make design recommendations for Search systems that will better serve the needs of this group.

Salehi, Mohsen Amini - One of the best experts on this subject based on the ideXlab platform.

  • SAED: Edge-Based Intelligence for Privacy-Preserving Enterprise Search on the Cloud
    2021
    Co-Authors: Salehi, Mohsen Amini, Buyya Rajkumar
    Abstract:

    Cloud-based Enterprise Search services (e.g., AWS Kendra) have been entrancing big data owners by offering convenient and real-time Search solutions to them. However, the problem is that individuals and organizations possessing confidential big data are hesitant to embrace such services due to valid data privacy concerns. In addition, to offer an intelligent Search, these services access the user Search history that further jeopardizes his/her privacy. To overcome the privacy problem, the main idea of this reSearch is to separate the intelligence aspect of the Search from its pattern matching aspect. According to this idea, the Search intelligence is provided by an on-premises edge tier and the shared cloud tier only serves as an exhaustive pattern matching Search utility. We propose Smartness At Edge (SAED mechanism that offers intelligence in the form of semantic and personalized Search at the edge tier while maintaining privacy of the Search on the cloud tier. At the edge tier, SAED uses a knowledge-based lexical database to expand the query and cover its semantics. SAED personalizes the Search via an RNN model that can learn the user interest. A word embedding model is used to retrieve documents based on their semantic relevance to the Search query. SAED is generic and can be plugged into existing Enterprise Search systems and enable them to offer intelligent and privacy-preserving Search without enforcing any change on them. Evaluation results on two Enterprise Search systems under real settings and verified by human users demonstrate that SAED can improve the relevancy of the retrieved results by on average 24% for plain-text and 75% for encrypted generic datasets

  • Privacy-Preserving Clustering of Unstructured Big Data for Cloud-Based Enterprise Search Solutions
    2020
    Co-Authors: Sm Zobaed, Gottmukkala Raju, Salehi, Mohsen Amini
    Abstract:

    Cloud-based Enterprise Search services (e.g., Amazon Kendra) are enchanting to big data owners by providing them with convenient Search solutions over their Enterprise big datasets. However, individuals and businesses that deal with confidential big data (eg, credential documents) are reluctant to fully embrace such services, due to valid concerns about data privacy. Solutions based on client-side encryption have been explored to mitigate privacy concerns. Nonetheless, such solutions hinder data processing, specifically clustering, which is pivotal in dealing with different forms of big data. For instance, clustering is critical to limit the Search space and perform real-time Search operations on big datasets. To overcome the hindrance in clustering encrypted big data, we propose privacy-preserving clustering schemes for three forms of unstructured encrypted big datasets, namely static, semi-dynamic, and dynamic datasets. To preserve data privacy, the proposed clustering schemes function based on statistical characteristics of the data and determine (A) the suitable number of clusters and (B) appropriate content for each cluster. Experimental results obtained from evaluating the clustering schemes on three different datasets demonstrate between 30% to 60% improvement on the clusters' coherency compared to other clustering schemes for encrypted data. Employing the clustering schemes in a privacy-preserving Enterprise Search system decreases its Search time by up to 78%, while increases the Search accuracy by up to 35%.Comment: arXiv admin note: text overlap with arXiv:1908.0496