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Combined geo-social search: computing top-k join queries over incomplete information

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Abstract

Geo-social data sets, which fuse the social and the geospatial facets of data, are vibrant data sources that associate people and activities with locations. In a combined geo-social search, several search queries are posed over geospatial and social data sources, or over data sources with both geospatial and social facets; and the search results, provided as ranked lists of items, are integrated by associating matching items, yielding combinations. Each combination has a score which is a function of the scores of the items it comprises, and the goal is to compute the k combinations with the highest score, that is, the top-k combinations. However, since geo-social data sources are heterogeneous, data items may not have matching items in all the ranked lists. Such items cannot be included in complete combinations. Hence, we study the approach where combinations are padded by nulls for missing items, as in outer-join. A combination is maximal if it cannot be extended by replacing a null by an item. We show that if some of the top-k maximal combinations contain null values, the computation requires reading entire lists, and hence, traditional top-k algorithms and optimization techniques are not as effective as in the case of an ordinary top-k join. Thus, we present two novel algorithms for computing the top-k maximal combinations. One novel algorithm is instance optimal over the class of algorithms that compute a 𝜃-approximation to the answer. The second algorithm is more efficient than the modification of two common top-k algorithms to compute maximal combinations. We show this analytically, and experimentally over real and synthetic data.

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Notes

  1. For instance, the geo-location API of Twitter provides the ability to attach geographic metadata to tweets. See http://twitter.com.

  2. Setting the score of nulls to be zero is arbitrary and any value that is smaller than the scores of non-null items can also be used. It makes sense to set this value to zero because a lacking item should not increase the score of a combination. Another alternative is to use a scoring function which is the multiplication of the item scores and in this case, all the item scores should be greater than 1 and the score of nulls should be equal to 1. All the results in this paper will remain unchanged in such a case.

  3. A preliminary version of the hybrid approach was presented in [66].

  4. Algorithms are instance optimal up to a factor that equals the number of lists.

  5. http://local.yahoo.com/

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Acknowledgements

This research was cnducted while Yaron Kanza was a visiting assistant professor at Jacobs Institute, Cornell Tech. This research was supported in part by the Israel Science Foundation (Grant 1467/13) and by the Isreali Ministry of Science and Technology (Grant 3-9617). We thank the anonymous reviewers for their insightful comments and suggestions.

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  1. AT&T Labs - Research, Bedminster, NJ, USA

    Yaron Kanza

  2. Department of Computer Science, Technion - Israel Institute of Technology, Haifa, Israel

    Mirit Shalem

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Kanza, Y., Shalem, M. Combined geo-social search: computing top-k join queries over incomplete information. Geoinformatica 22, 615–660 (2018). https://doi.org/10.1007/s10707-017-0297-y

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