A time machine for text search

Berberich, Klaus; Bedathur, Srikanta; Neumann, Thomas; Weikum, Gerhard

doi:10.1145/1277741.1277831

Cited by 68 publications

(81 citation statements)

References 31 publications

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“…The work of Berberich et al [1] deals with the problem of reducing a temporal relation, where each tuple is assigned a real value and no temporal gaps or aggregation groups exist. The authors propose a technique called approximate temporal coalescing (ATC) which merges adjacent tuples if the local merge error does not exceed a user-specified threshold.…”

Section: Related Workmentioning

confidence: 99%

“…The average salary is then computed as (2·800+3·825)/(5−1+1) = 815. The final result of merging these two tuples is s1 ⊕ s2 = (A, 815, 28.5, [1,5]). …”

Section: Definition 4 (Merge Operator)mentioning

confidence: 99%

“…• An experimental evaluation of PTA on synthetic and real-world data revealed the following results: considerable reductions of the result size introduce only small errors; gPTA is scalable for large data sets, it has an error that is very close to the exact PTA solution, and it is consistently better than the approximate temporal coalescing approach in [1].…”

Section: Introductionmentioning

confidence: 98%

“…. , s4 are merged in three steps to produce the PTA result tuple z1, stating that in the interval [1,10] the average salary and working hours for project A are 840 and 29, respectively.…”

Section: Introductionmentioning

confidence: 99%

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Parsimonious temporal aggregation

2011

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Temporal aggregation is a crucial operator in temporal databases and has been studied in various flavors, including instant temporal aggregation (ITA) and span temporal aggregation (STA), each having its strengths and weaknesses. In this paper we define a new temporal aggregation operator, called parsimonious temporal aggregation (PTA), which comprises two main steps: (i) it computes the ITA result over the input relation and (ii) it compresses this intermediate result to a user-specified size c by merging adjacent tuples and keeping the induced total error minimal; the compressed ITA result is returned as the final result. By considering the distribution of the input data and allowing to control the result size, PTA combines the best features of ITA and STA. We provide two evaluation algorithms for PTA queries. First, the oPTA algorithm computes an exact solution, by applying dynamic programming to explore all possibilities to compress the ITA result and selecting the compression with the minimal total error. It runs in O(n2pc) time and O(n2) space, where n is the size of the input relation and p is the number of aggregation functions in the query. Second, the more efficient gPTA algorithm computes an approximate solution by greedily merging the most similar ITA result tuples, which, however, does not guarantee a compression with a minimal total error. gPTA intermingles the two steps of PTA and avoids large intermediate results. The compression step of gPTA runs in O(np log(c + ?)) time and O(c + ?) space, where ? is a small buffer for ""look ahead"". An empirical evaluation shows good results: considerable reductions of the result size introduce only small errors, and gPTA scales to large data sets and is only slightly worse than the exact solution of PTA. Parsimonious Temporal Aggregation Juozas Gordevičius Johann Gamper Michael BöhlenFree University of Bozen-Bolzano, Italy {gordevicius,gamper,boehlen}@inf.unibz.it ABSTRACTTemporal aggregation is a crucial operator in temporal databases and has been studied in various flavors, including instant temporal aggregation (ITA) and span temporal aggregation (STA), each having its strengths and weaknesses. In this paper we define a new temporal aggregation operator, called parsimonious temporal aggregation (PTA), which comprises two main steps: (i) it computes the ITA result over the input relation and (ii) it compresses this intermediate result to a user-specified size c by merging adjacent tuples and keeping the induced total error minimal; the compressed ITA result is returned as the final result. By considering the distribution of the input data and allowing to control the result size, PTA combines the best features of ITA and STA. We provide two evaluation algorithms for PTA queries. First, the oPTA algorithm computes an exact solution, by applying dynamic programming to explore all possibilities to compress the ITA result and selecting the compression with the minimal total error. It runs in O(n 2 pc) time and O(n 2 ) space, where n is the size of th...

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Section: Related Workmentioning

confidence: 99%

“…The average salary is then computed as (2·800+3·825)/(5−1+1) = 815. The final result of merging these two tuples is s1 ⊕ s2 = (A, 815, 28.5, [1,5]). …”

Section: Definition 4 (Merge Operator)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

“…. , s4 are merged in three steps to produce the PTA result tuple z1, stating that in the interval [1,10] the average salary and working hours for project A are 840 and 29, respectively.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Parsimonious temporal aggregation

2011

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“…In order to increase precision, a user can narrow down search results by extending query keywords with the creation or update date of documents (called temporal criteria). Two ways of obtaining temporal criteria relevant to a query are 1) having them provided by the user [1,6], or 2) determined by the system [5]. One way of increasing recall is to perform query expansion using synonyms.…”

Section: Introductionmentioning

confidence: 99%

QUEST: Query Expansion Using Synonyms over Time

Kanhabua

Nørvåg

2010

Machine Learning and Knowledge Discovery in Databases

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Abstract.A particular problem of searching news archives with named entities is that they are very dynamic in appearance compared to other vocabulary terms, and synonym relationships between terms change with time. In previous work, we proposed an approach to extracting time-based synonyms of named entities from the whole history of Wikipedia. In this paper, we present QUEST (Query Expansion using Synonyms over Time), a system that exploits time-based synonyms in searching news archives. The system takes as input a named entity query, and automatically determines time-based synonyms for a given query wrt. time criteria. Query expansion of the determined synonyms can be employed in order to improve the retrieval effectiveness.

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Modeling geographic, temporal, and proximity contexts for improving geotemporal search

Daoud

Huang

2012

J Am Soc Inf Sci Tec

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Traditional information retrieval (IR) systems show significant limitations on returning relevant documents thatsatisfy the user's information needs. In particular, to answer geographic and temporal user queries, the IR task becomes a nonstraightforward process where the available geographic and temporal information is often unstructured. In this article, we propose a geotemporal search approach that consists of modeling and exploiting geographic and temporal query context evidence that refers to implicit multivarying geographic and temporal intents behind the query. Modeling geographic and temporal query contexts is based on extracting and ranking geographic and temporal keywords found in pseudo-relevant feedback (PRF) documents for a given query. Our geotemporal search approach is based on exploiting the geographic and temporal query contexts separately into a probabilistic ranking model and jointly into a proximity ranking model. Our hypothesis is based on the concept that geographic and temporal expressions tend to co-occur within the document where the closer they are in the document, the more relevant the document is. Finally, geographic, temporal, and proximity scores are combined according to a linear combination formula. An extensive experimental evaluation conducted on a portion of the New York Times news collection and the TREC 2004 robust retrieval track collection shows that our geotemporal approach outperforms significantly a well-known baseline search and the best known geotemporal search approaches in the domain. Finally, an in-depth analysis shows a positive correlation between the geographic and temporal query sensitivity and the retrieval performance. Also, we find that geotemporal distance has a positive impact on retrieval performance generally.

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A time machine for text search

Cited by 68 publications

References 31 publications

Parsimonious temporal aggregation

Parsimonious temporal aggregation

QUEST: Query Expansion Using Synonyms over Time

Modeling geographic, temporal, and proximity contexts for improving geotemporal search

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