Efficient influence-based processing of market research queries

Arvanitis, Anastasios; Deligiannakis, Antonios; Vassiliou, Yannis

doi:10.1145/2396761.2398420

Cited by 19 publications

(44 citation statements)

References 17 publications

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“…The authors propose an algorithm that selects the subset of attributes that increases the visibility of a new product. Customer identification and product positioning has been recently studied in [2], where the attractiveness of a product is defined based on the concept of reverse skyline query.…”

Section: Related Workmentioning

confidence: 99%

Branch-and-bound algorithm for reverse top-k queries

Vlachou

Doulkeridis

Nørvåg

et al. 2013

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

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Top-k queries return to the user only the k best objects based on the individual user preferences and comprise an essential tool for rank-aware query processing. Assuming a stored data set of user preferences, reverse top-k queries have been introduced for retrieving the users that deem a given database object as one of their top-k results. Reverse top-k queries have already attracted significant interest in research, due to numerous real-life applications such as market analysis and product placement. Currently, the most efficient algorithm for computing the reverse top-k set is RTA. RTA has two main drawbacks when processing a reverse top-k query: (i) it needs to access all stored user preferences, and (ii) it cannot avoid executing a top-k query for each user preference that belongs to the result set. To address these limitations, in this paper, we identify useful properties for processing reverse top-k queries without accessing each user's individual preferences nor executing the topk query. We propose an intuitive branch-and-bound algorithm for processing reverse top-k queries efficiently and discuss novel optimizations to boost its performance. Our experimental evaluation demonstrates the efficiency of the proposed algorithm that outperforms RTA by a large margin.

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Section: Related Workmentioning

confidence: 99%

Branch-and-bound algorithm for reverse top-k queries

Vlachou

Doulkeridis

Nørvåg

et al. 2013

Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data

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“…. , d n } and S be a set of data objects on D. A data object is represented as a point o = {o [1], . .…”

Section: Preliminariesmentioning

confidence: 99%

“…In this paper, we adopt the definition of influence that was first introduced by Vlachou et al [17], where the influential objects are those that appear in the top-k lists of many users, i.e., have the larger reverse top-k results. A different definition of influence is used in [1], where the authors try to discover attractive products to users using the principle of skyline sets [3]. Other approaches try to identify the attributes of products that maximize its visibility [11] or the region in the space defined by the products' attributes where a product can be promoted [19,20].…”

Section: Related Workmentioning

confidence: 99%

Discovering Influential Data Objects over Time

Gkorgkas

Vlachou

Doulkeridis

et al. 2013

Advances in Spatial and Temporal Databases

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Abstract. In applications such as market analysis, it is of great interest to product manufacturers to have their products ranked as highly as possible for a significant number of customers. However, customer preferences change over time, and product manufacturers are interested in monitoring the evolution of the popularity of their products, in order to discover those products that are consistently highly ranked. To take into account the temporal dimension, we define the continuous influential query and present algorithms for efficient processing and retrieval of continuous influential data objects. Furthermore, our algorithms support incremental retrieval of the next continuous influential data object in a natural way. To evaluate the performance of our algorithms, we conduct a detailed experimental study for various setups.

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“…The concept of top-k query for user preferences is presented in Refs. [14][15][16][17][18], but what they are focusing is reverse top-k query not top-k query. So it is significant to study the top-k query based on user preferences.…”

Section: Introductionmentioning

confidence: 99%