Reverse Top-k Research Articles

Durable reverse top-k queries on time-varying preference

Durable reverse top-k queries on time-varying preference

Answering Non-Answer Questions on Reverse Top-k Geo-Social Keyword Queries

Answering Non-Answer Questions on Reverse Top-k Geo-Social Keyword Queries

Private Reverse Top-k Algorithms Applied on Public Data of COVID-19 in the State of Ceará

In this article we propose a differentially private reverse top-k query. Our strategy allows obtaining the less frequent data according to a search criteria, with a high guarantee of privacy of the individuals who contributed with personal data in the original database. We apply our strategy on public data for COVID-19 in the State of Ceará using two different queries. Our experimental results show that the result of the proposed top-k query returns a high degree of similarity to the result of a conventional top-k query, when the chosen budget is suitable, providing useful results for researchers, while ensuring a low probability of re-identification of individuals arising from the properties of differential privacy.

Pruning techniques for parallel processing of reverse top-k queries

In this paper, we address the problem of processing reverse top-k queries in a parallel setting. Given a database of objects, a set of user preferences, and a query object q, the reverse top-k query returns the subset of user preferences for which the query object belongs to the top-k results. Although recently the reverse top-k query operator has been studied extensively, its CPU-intensive nature results in prohibitively expensive processing cost, when applied on vast-sized data sets. This limitation motivates us to explore a scalable parallel processing solution, in order to enable reverse top-k processing over distributed large sets of input data in reasonable execution time. We present an algorithmic framework for the problem, in which different algorithms can be instantiated, targeting a generic parallel setting. We describe a parallel algorithm (DiPaRT) that exploits basic pruning properties and is provably correct, as an instantiation of the framework. Furthermore, we introduce novel pruning properties for the problem, and propose DiPaRT+ as another instance of the algorithmic framework, which offers improved efficiency and scales gracefully. All algorithms are implemented in MapReduce, and we provide a wide set of experiments that demonstrate the improved efficiency of DiPaRT+ using data sets that are four orders of magnitude larger than those handled by centralized approaches.

An Efficient Policy User Web Page Correspondence Searching for Support Query Rewriting

Nowadays, Social media positions remain a favorite combination as clients studying to distribute their occurrences, activities on the network. These websites receive large quantities of user-supplied elements during the vast difference natural world results of various varieties, reach. An effective advertisement marketing approach is designed by using the products that contain the information, additionally client inclinations and conclusions on information about products. By this kind of similarity between concepts of profile, a precise matching method is developed to match the profile of the Web services and user. In this work, the approach is developed to search the concept similarity in the second phase of the process of query rewriting is performed after extraction queries. Similarity measure techniques are very much useful in processing database queries such as top-k queries, reverse top-k queries, k-nearest neighbor queries and other different types of queries related to trading sales activities.

Oniomania Similarity Search with Recommended System

Users shopping online prefer to quickly access products that they are in need of among a list of similar products. The similarity metrics in use, only contemplates on the product attributes which doesn’t always meet the user expectations. The current system relies on solving this by performing Θ-similarity and r-nearest neighbor, where product similarity is considered only if it satisfies a user-preference list, by hitting the reverse top-k queries results. However, the products expressed here are generally based on the user’s preference, which can break the user independence of the product. To conquer these drawbacks, we use a more advanced system which represents feature based products such as Usercentric Model, where we have two phases. At first, we gather opinion based data about the feature, and subsequently these feature similarities are ranked. The ranking is predicted by collaborative filtering recommendation algorithm. Next, the rank generated from the above algorithm will be compared with existing data to get the top-k best product by performing No-Random Access algorithm (NRA) in second phase. Through this users are granted the top.

An efficient method for the reverse top k search

The reverse top-k search problem is a variant of the top-k search problem. Suppose that there are a set of objects O and a set of weight vectors W in the d-dimensional space. Then, given a query object, the reverse top-k(RTOPk) search is to find a subset W′ of W such that, when the query object is inserted into O(or already included in O), the query object is one of the top-k objects for each weight vector in W′. Many studies on the RTOPk search have been conducted in the last few years. However, the existing methods for the RTOPk search only focus on how to efficiently process evaluations of the weight vectors, which check whether the weight vectors are included in the result without efficient pruning methods. In this paper, we propose an efficient method for the RTOPk search, that overcomes drawbacks of existing methods. In contrast to existing methods, the proposed method prunes each unnecessary weight vector without traversing objects, and filters out each unnecessary object without considering the weight vectors. In addition, the proposed method evaluates weight vectors by preserving search contexts for multiple traversals to tree-based indexes of weight vectors and objects. The experimental results based on synthetic datasets and a real dataset show that the proposed method is significantly more efficient than existing methods.

Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries

Reverse top-k queries are proposed from the perspective of a product manufacturer, which are essential for manufacturers to assess the potential market. However, the existing approaches for reverse top-k queries are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods cannot be applied to process uncertain data sets directly. Motivated by this, in this paper, we firstly model the probabilistic reverse top-k queries over uncertain data. Moreover, we formulate a probabilistic top-l influential query, that reports the l most influential objects having the largest impact factors, where the impact factor of an object is defined as the cardinality of its probabilistic reverse top-k query result set. We present effective pruning heuristics for speeding up the queries. Particularly, we exploit several properties of probabilistic threshold top-k queries and probabilistic skyline queries to reduce the search space of this problem. In addition, an upper bound of the potential users is estimated to reduce the cost of computing the probabilistic reverse top-k queries for the candidate objects. Finally, efficient query algorithms are presented seamlessly with integration of the proposed pruning strategies. Extensive experiments using both real-world and synthetic data sets demonstrate the efficiency and effectiveness of our proposed algorithms.

Hub Labels on the database for large-scale graphs with the COLD framework

Shortest-path computation on graphs is one of the most well-studied problems in algorithmic theory. An aspect that has only recently attracted attention is the use of databases in combination with graph algorithms, so-called distance oracles, to compute shortest-path queries on large graphs. To this purpose, we propose a novel, efficient, pure-SQL framework for answering exact distance queries on large-scale graphs, implemented entirely on an open-source database engine. Our COLD framework (COmpressed Labels on the Database) can answer multiple distance queries (vertex-to-vertex, one-to-many, k-Nearest Neighbors, Reverse k-Nearest Neighbors, Reverse k-Farthest Neighbors and Top-k Range) not handled by previous methods, rendering it a complete database solution for a variety of practical large-scale graph applications. Our experimentation shows that COLD outperforms existing approaches (including popular graph databases) in terms of query time and efficiency, while requiring significantly less storage space than these methods.

Reverse k nearest neighbors queries and spatial reverse top-k queries

Given a set of facilities and a set of users, a reverse k nearest neighbors (RkNN) query q returns every user for which the query facility is one of the k closest facilities. Almost all of the existing techniques to answer RkNN queries adopt a pruning-and-verification framework. Regions-based pruning and half-space pruning are the two most notable pruning strategies. The half-space-based approach prunes a larger area and is generally believed to be superior. Influenced by this perception, almost all existing RkNN algorithms utilize and improve the half-space pruning strategy. We observe the weaknesses and strengths of both strategies and discover that the regions-based pruning has certain strengths that have not been exploited in the past. Motivated by this, we present a new regions-based pruning algorithm called Slice that utilizes the strength of regions-based pruning and overcomes its limitations. We also study spatial reverse top-k (SRTk) queries that return every user u for which the query facility is one of the top-k facilities according to a given linear scoring function. We first extend half-space-based pruning to answer SRTk queries. Then, we propose a novel regions-based pruning algorithm following Slice framework to solve the problem. Our extensive experimental study on synthetic and real data sets demonstrates that Slice is significantly more efficient than all existing RkNN and SRTk algorithms.

Answering why-not and why questions on reverse top-k queries

Why-not and why questions can be posed by database users to seek clarifications on unexpected query results. Specifically, why-not questions aim to explain why certain expected tuples are absent from the query results, while why questions try to clarify why certain unexpected tuples are present in the query results. This paper systematically explores the why-not and why questions on reverse top-k queries, owing to its importance in multi-criteria decision making. We first formalize why-not questions on reverse top-k queries, which try to include the missing objects in the reverse top-k query results, and then, we propose a unified framework called WQRTQ to answer why-not questions on reverse top-k queries. Our framework offers three solutions to cater for different application scenarios. Furthermore, we study why questions on reverse top-k queries, which aim to exclude the undesirable objects from the reverse top-k query results, and extend the framework WQRTQ to efficiently answer why questions on reverse top-k queries, which demonstrates the flexibility of our proposed algorithms. Extensive experimental evaluation with both real and synthetic data sets verifies the effectiveness and efficiency of the presented algorithms under various experimental settings.

Efficient monochromatic and bichromatic probabilistic reverse top-k query processing for uncertain big data

There has been an increasing growth in numerous applications that naturally generate large volumes of uncertain data. By the advent of such applications, the support of advanced analysis query processing such as the top-k and reverse top-k for uncertain big data has become important. In this paper, we model firstly probabilistic reverse top-k queries over uncertain big data for the discrete situation, in both monochromatic and bichromatic cases, denoted by MPRT and BPRT queries, respectively. We determine the partitions of solution space of MPRT queries and provide in theory a mathematical model for solving arbitrary dimensional data space. Additionally, we propose effective pruning heuristics to reduce the search space of BPRT queries. Moreover, efficient query procedures are presented seamlessly with integration of the proposed pruning strategies. Extensive experiments demonstrate the efficiency and effectiveness of our proposed approaches with various experimental settings.

Towards efficient top-k reliability search on uncertain graphs

Uncertain graph has been widely used to represent graph data with inherent uncertainty in structures. Reliability search is a fundamental problem in uncertain graph analytics. This paper investigates on a new problem with broad real-world applications, the top-k reliability search problem on uncertain graphs, that is, finding the k vertices v with the highest reliabilities of connections from a source vertex s to v. Note that the existing algorithm for the threshold-based reliability search problem is inefficient for the top-k reliability search problem. We propose a new algorithm to efficiently solve the top-k reliability search problem. The algorithm adopts two important techniques, namely the BFS sharing technique and the offline sampling technique. The BFS sharing technique exploits overlaps among different sampled possible worlds of the input uncertain graph and performs a single BFS on all possible worlds simultaneously. The offline sampling technique samples possible worlds offline and stores them using a compact structure. The algorithm also takes advantages of bit vectors and bitwise operations to improve efficiency. In addition, we generalize the top-k reliability search problem from single-source case to the multi-source case and show that the multi-source case of the problem can be equivalently converted to the single-source case of the problem. Moreover, we define two types of the reverse top-k reliability search problems with different semantics on uncertain graphs. We propose appropriate solutions for both of them. Extensive experiments carried out on both real and synthetic datasets verify that the optimized algorithm outperforms the baselines by 1---2 orders of magnitude in execution time while achieving comparable accuracy. Meanwhile, the optimized algorithm exhibits linear scalability with respect to the size of the input uncertain graph.

Monochromatic and bichromatic reverse top-k group nearest neighbor queries

The Group Nearest Neighbor (GNN) search is an important approach for expert and intelligent systems, i.e., Geographic Information System (GIS) and Decision Support System (DSS). However, traditional GNN search starts from users’ perspective and selects the locations or objects that users like. Such applications fail to help the managers since they do not provide managerial insights. In this paper, we focus on solving the problem from the managers’ perspective. In particular, we propose a novel GNN query, namely, the reverse top-k group nearest neighbor (RkGNN) query which returns k groups of data objects so that each group has the query object q as their group nearest neighbor (GNN). This query is an important tool for decision support, e.g., location-based service, product data analysis, trip planning, and disaster management because it provides data analysts an intuitive way for finding significant groups of data objects with respect to q. Despite their importance, this kind of queries has not received adequate attention from the research community and it is a challenging task to efficiently answer the RkGNN queries. To this end, we first formalize the reverse top-k group nearest neighbor query in both monochromatic and bichromatic cases, and then propose effective pruning methods, i.e., sorting and threshold pruning, MBR property pruning, and window pruning, to reduce the search space during the RkGNN query processing. Furthermore, we improve the performance by employing the reuse heap technique. As an extension to the RkGNN query, we also study an interesting variant of the RkGNN query, namely a constrained reverse top-k group nearest neighbor (CRkGN) query. Extensive experiments using synthetic and real datasets demonstrate the efficiency and effectiveness of our approaches.

Efficient Reverse Top-<italic>k</italic> Boolean Spatial Keyword Queries on Road Networks

Reverse k nearest neighbor (R k NN) queries have a broad application base such as decision support, profile-based marketing, and resource allocation. Previous work on R k NN search does not take textual information into consideration or limits to the Euclidean space. In the real world, however, most spatial objects are associated with textual information and lie on road networks. In this paper, we introduce a new type of queries, namely, reverse top-k Boolean spatial keyword (R k BSK) retrieval , which assumes objects are on the road network and considers both spatial and textual information. Given a data set P on a road network and a query point q with a set of keywords, an R k BSK query retrieves the points in P that have q as one of answer points for their top- k Boolean spatial keyword queries. We formalize the R k BSK query and then propose filter-and-refinement framework based algorithms for answering R k BSK search with arbitrary k and no any pre-computation . To accelerate the query process, several novel pruning heuristics that utilize both spatial and textual information are employed to shrink the search space efficiently. In addition, a new data structure called count tree has been developed to further improve query performance. A comprehensive experimental evaluation using both real and synthetic data sets demonstrates the effectiveness of our presented pruning heuristics and the performance of our proposed algorithms.

Answering why-not questions on reverse top- k queries

Why-not questions, which aim to seek clarifications on the missing tuples for query results, have recently received considerable attention from the database community. In this paper, we systematically explore why-not questions on reverse top-k queries , owing to its importance in multi-criteria decision making. Given an initial reverse top- k query and a missing/why-not weighting vector set W m that is absent from the query result, why-not questions on reverse top- k queries explain why W m does not appear in the query result and provide suggestions on how to refine the initial query with minimum penalty to include W m in the refined query result. We first formalize why-not questions on reverse top- k queries and reveal their semantics, and then propose a unified framework called WQRTQ to answer why-not questions on both monochromatic and bichromatic reverse top- k queries. Our framework offers three solutions, namely, (i) modifying a query point q , (ii) modifying a why-not weighting vector set W m and a parameter k , and (iii) modifying q , W m , and k simultaneously, to cater for different application scenarios. Extensive experimental evaluation using both real and synthetic data sets verifies the effectiveness and efficiency of the presented algorithms.

Reverse top-k search using random walk with restart

With the increasing popularity of social networks, large volumes of graph data are becoming available. Large graphs are also derived by structure extraction from relational, text, or scientific data (e.g., relational tuple networks, citation graphs, ontology networks, protein-protein interaction graphs). Node-to-node proximity is the key building block for many graph-based applications that search or analyze the data. Among various proximity measures, random walk with restart (RWR) is widely adopted because of its ability to consider the global structure of the whole network. Although RWR-based similarity search has been well studied before, there is no prior work on reverse top- k proximity search in graphs based on RWR. We discuss the applicability of this query and show that its direct evaluation using existing methods on RWR-based similarity search has very high computational and storage demands. To address this issue, we propose an indexing technique, paired with an on-line reverse top- k search algorithm. Our experiments show that our technique is efficient and has manageable storage requirements even when applied on very large graphs.

Efficient All Top-k Computation - A Unified Solution for All Top-k, Reverse Top-k and Top-m Influential Queries

Given a set of objects P and a set of ranking functions F over P, an interesting problem is to compute the top ranked objects for all functions. Evaluation of multiple top-k queries finds application in systems, where there is a heavy workload of ranking queries (e.g., online search engines and product recommendation systems). The simple solution of evaluating the top-k queries one-by-one does not scale well; instead, the system can make use of the fact that similar queries share common results to accelerate search. This paper is the first, to our knowledge, thorough study of this problem. We propose methods that compute all top-k queries in batch. Our first solution applies the block indexed nested loops paradigm, while our second technique is a view-based algorithm. We propose appropriate optimization techniques for the two approaches and demonstrate experimentally that the second approach is consistently the best. Our approach facilitates evaluation of other complex queries that depend on the computation of multiple top-k queries, such as reverse top-k and top-m influential queries. We show that our batch processing technique for these complex queries outperform the state-of-the-art by orders of magnitude.

Monochromatic and Bichromatic Reverse Top-k Queries

Nowadays, most applications return to the user a limited set of ranked results based on the individual user's preferences, which are commonly expressed through top-k queries. From the perspective of a manufacturer, it is imperative that her products appear in the highest ranked positions for many different user preferences, otherwise the product is not visible to potential customers. In this paper, we define a novel query type, namely the reverse top-k query, that covers this requirement: “Given a potential product, which are the user preferences that make this product belong to the top-k query result set?.” Reverse top-k queries are essential for manufacturers to assess the impact of their products in the market based on the competition. We formally define reverse top-k queries and introduce two versions of the query, monochromatic and bichromatic. First, we provide a geometric interpretation of the monochromatic reverse top-k query to acquire an intuition of the solution space. Then, we study in detail the case of bichromatic reverse top-k query, and we propose two techniques for query processing, namely an efficient threshold-based algorithm and an algorithm based on materialized reverse top-k views. Our experimental evaluation demonstrates the efficiency of our techniques.

Identifying the most influential data objects with reverse top-k queries

Top- k queries are widely applied for retrieving a ranked set of the k most interesting objects based on the individual user preferences. As an example, in online marketplaces, customers (users) typically seek a ranked set of products (objects) that satisfy their needs. Reversing top- k queries leads to a query type that instead returns the set of customers that find a product appealing (it belongs to the top- k result set of their preferences). In this paper, we address the challenging problem of processing queries that identify the top- m most influential products to customers, where influence is defined as the cardinality of the reverse top- k result set. This definition of influence is useful for market analysis, since it is directly related to the number of customers that value a particular product and, consequently, to its visibility and impact in the market. Existing techniques require processing a reverse top- k query for each object in the database, which is prohibitively expensive even for databases of moderate size. In contrast, we propose two algorithms, SB and BB , for identifying the most influential objects: SB restricts the candidate set of objects that need to be examined, while BB is a branch-and-bound algorithm that retrieves the result incrementally. Furthermore, we propose meaningful variations of the query for most influential objects that are supported by our algorithms. Our experiments demonstrate the efficiency of our algorithms both for synthetic and real-life datasets.