Build your complex search: social, behavioral, andmicro-economic perspective on modern Web search
15 Jun 2011-
TL;DR: This work investigates the social, economic, and behavioral trends that push towards a completely different interpretation of the search task on the Web and discusses how the technology and the micro-economic models must change to face these challenges.
Abstract: So far, Web search has been a playground for few giants
However, while traditional search engines are superb in their
ability of extracting the Web pages that most closely match with
user’s keywords, they fail in going beyond such simple paradigm
On the other side, an increasing number of data sets is becoming
available on the Web as (semi) structured data instead of userconsumable
pages Web search has huge potentials for
improvement thanks to the high quality of these data sources, but
this can be achieved only by designing new search applications
that federate those sources To tackle the long tail of user
requirements and tastes, the need arises for new ways of thinking
and designing search applications: application providers (and
perhaps even end users) will need to build their own, customized
search experiences, by combining search services available on the
Web at the purpose of solving specific search needs Individual
and collective social experience will be more and more
influencing search results In our work we investigate the social,
economic, and behavioral trends that push towards a completely
different interpretation of the search task on the Web We discuss
how the technology and the micro-economic models must change
to face these challenges
References
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TL;DR: This survey describes and classify top-k processing techniques in relational databases including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions, and shows the implications of each dimension on the design of the underlying techniques.
Abstract: Efficient processing of top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient top-k processing in domains such as the Web, multimedia search, and distributed systems has shown a great impact on performance. In this survey, we describe and classify top-k processing techniques in relational databases. We discuss different design dimensions in the current techniques including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions. We show the implications of each dimension on the design of the underlying techniques. We also discuss top-k queries in XML domain, and show their connections to relational approaches.
893 citations
"Build your complex search: social, ..." refers methods in this paper
...When data sources must be joined, the join operation must take into account ranking; join can either be based on exact methods, according to the rank-join theory, or on approximate methods, that favor the speed of result production [7]....
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TL;DR: This lecture introduces exploratory search, relates it to relevant extant research, outline the features of exploratorySearch systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratorysearch.
Abstract: As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model support d by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world. Table of Contents: Introduction / Defining Exploratory Search / Related Work / Features of Exploratory Search Systems / Evaluation of Exploratory Search Systems / Future Directions and concluding Remarks
725 citations
26 Oct 2008
TL;DR: A novel "navigational" expectation that's particularly useful in the context of faceted search, and a novel interestingness measure through judicious application of p-values are proposed.
Abstract: We propose a dynamic faceted search system for discovery-driven analysis on data with both textual content and structured attributes. From a keyword query, we want to dynamically select a small set of "interesting" attributes and present aggregates on them to a user. Similar to work in OLAP exploration, we define "interestingness" as how surprising an aggregated value is, based on a given expectation. We make two new contributions by proposing a novel "navigational" expectation that's particularly useful in the context of faceted search, and a novel interestingness measure through judicious application of p-values. Through a user survey, we find the new expectation and interestingness metric quite effective. We develop an efficient dynamic faceted search system by improving a popular open source engine, Solr. Our system exploits compressed bitmaps for caching the posting lists in an inverted index, and a novel directory structure called a bitset tree for fast bitset intersection. We conduct a comprehensive experimental study on large real data sets and show that our engine performs 2 to 3 times faster than Solr.
113 citations
"Build your complex search: social, ..." refers background in this paper
...A variety of different tools now exist for information exploration: dynamic faceted taxonomies [6], topic exploration (e....
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26 Apr 2010
TL;DR: The Liquid Query paradigm is proposed, to support users in finding responses to multi-domain queries through exploratory information seeking across structured information sources, wrapped by means of a uniform notion of search service.
Abstract: In this paper we propose the Liquid Query paradigm, to support users in finding responses to multi-domain queries through exploratory information seeking across structured information sources (Web documents, deep Web data, and personal data repositories), wrapped by means of a uniform notion of search service. Liquid Query aims at filling the gap between general-purpose search engines, which are unable to find information spanning multiple topics, and domain-specific search systems, which cannot go beyond their domain limits. The Liquid Query interface consists of interaction primitives that let users pose questions and explore results spanning over multiple sources incrementally, thus getting closer and closer to the sought information. We demonstrate our approach with a prototype built upon the YQL (Yahoo! Query Language) framework.
84 citations
"Build your complex search: social, ..." refers background in this paper
..., on service statistics), optimal result paging, and so on [1]....
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01 Aug 2008
TL;DR: This paper presents an overall framework for multi-domain queries on the Web, which adapts to a variety of application contexts, ranging from end-user-oriented mash-up scenarios up to complex application integration scenarios.
Abstract: Where can I attend an interesting database workshop close to a sunny beach? Who are the strongest experts on service computing based upon their recent publication record and accepted European projects? Can I spend an April weekend in a city served by a low-cost direct flight from Milano offering a Mahler's symphony? We regard the above queries as multi-domain queries, i.e., queries that can be answered by combining knowledge from two or more domains (such as: seaside locations, flights, publications, accepted projects, conference offerings, and so on). This information is available on the Web, but no general-purpose software system can accept the above queries nor compute the answer. At the most, dedicated systems support specific multi-domain compositions (e.g., Google-local locates information such as restaurants and hotels upon geographic maps).This paper presents an overall framework for multi-domain queries on the Web. We address the following problems: (a) expressing multi-domain queries with an abstract formalism, (b) separating the treatment of "search" services within the model, by highlighting their differences from "exact" Web services, (c) explaining how the same query can be mapped to multiple "query plans", i.e., a well-defined scheduling of service invocations, possibly in parallel, which complies with their access limitations and preserves the ranking order in which search services return results; (d) introducing cross-domain joins as first-class operation within plans; (e) evaluating the query plans against several cost metrics so as to choose the most promising one for execution. This framework adapts to a variety of application contexts, ranging from end-user-oriented mash-up scenarios up to complex application integration scenarios.
63 citations
"Build your complex search: social, ..." refers background in this paper
...Web search has huge potentials for improving the quality of search results thanks to the high quality of these data sources, but this can be achieved only by designing new search applications that federate and compose search services in the proper way, through complex and optimized Web query strategies [2]....
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..., complex optimization strategies and result composition logics [2], as described in section 4....
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