Geo-textual indices play an important role in spatial keyword querying. The existing geo-textual indices have not been compared systematically under the same experimental framework. This makes it difficult to determine which indexing technique best supports specific functionality. We provide an all-around survey of 12 state-of-the-art geo-textual indices. We propose a benchmark that enables the comparison of the spatial keyword query performance. We also report on the findings obtained when applying the benchmark to the indices, thus uncovering new insights that may guide index selection as well as further research.

/pdf/spatial-keyword-query-processing-an-experimental-evaluation-3hwgy94us9.pdf

Spatial keyword query processing: an experimental evaluation

The web is increasingly being used by mobile users. In addition, it is increasingly becoming possible to accurately geo-position mobile users and web content. This development gives prominence to spatial web data management. Specifically, a spatial keyword query takes a user location and user-supplied keywords as arguments and returns web objects that are spatially and textually relevant to these arguments. This paper reviews recent results by the authors that aim to achieve spatial keyword querying functionality that is easy to use, relevant to users, and can be supported efficiently. The paper covers different kinds of functionality as well as the ideas underlying their definition.

https://www3.ntu.edu.sg/home/gaocong/papers/er12.pdf

Spatial keyword querying

In this big data era, huge amounts of spatial documents have been generated everyday through various location based services. Top-k spatial keyword search is an important approach to exploring useful information from a spatial database. It retrieves k documents based on a ranking function that takes into account both textual relevance (similarity between the query and document keywords) and spatial relevance (distance between the query and document locations). Various hybrid indexes have been proposed in recent years which mainly combine the R-tree and the inverted index so that spatial pruning and textual pruning can be executed simultaneously. However, the rapid growth in data volume poses significant challenges to existing methods in terms of the index maintenance cost and query processing time.In this paper, we propose a scalable integrated inverted index, named I3, which adopts the Quadtree structure to hierarchically partition the data space into cells. The basic unit of I3 is the keyword cell, which captures the spatial locality of a keyword. Moreover, we design a new storage mechanism for efficient retrieval of keyword cell and preserve additional summary information to facilitate pruning. Experiments conducted on real spatial datasets (Twitter and Wikipedia) demonstrate the superiority of I3 over existing schemes such as IR-tree and S2I in various aspects: it incurs shorter construction time to build the index, it has lower index storage cost, it is order of magnitude faster in updates, and it is highly scalable and answers top-k spatial keyword queries efficiently.

/pdf/scalable-top-k-spatial-keyword-search-4ddps2sf92.pdf

Scalable top-k spatial keyword search

With advances in geo-positioning technologies and geo-location services, there are a rapidly growing amount of spatio-textual objects collected in many applications such as location based services and social networks, in which an object is described by its spatial location and a set of keywords (terms). Consequently, the study of spatial keyword search which explores both location and textual description of the objects has attracted great attention from the commercial organizations and research communities. In the paper, we study the problem of top k spatial keyword search (TOPK-SK), which is fundamental in the spatial keyword queries. Given a set of spatio-textual objects, a query location and a set of query keywords, the top k spatial keyword search retrieves the closest k objects each of which contains all keywords in the query. Based on the inverted index and the linear quadtree, we propose a novel index structure, called inverted linear quadtree (IL-Quadtree), which is carefully designed to exploit both spatial and keyword based pruning techniques to effectively reduce the search space. An efficient algorithm is then developed to tackle top k spatial keyword search. In addition, we show that the IL-Quadtree technique can also be applied to improve the performance of other spatial keyword queries such as the direction-aware top k spatial keyword search and the spatio-textual ranking query. Comprehensive experiments on real and synthetic data clearly demonstrate the efficiency of our methods.

Inverted linear quadtree: Efficient top k spatial keyword search

Recently, spatial keyword queries become a hot topic in the literature. One example of these queries is the collective spatial keyword query (CoSKQ) which is to find a set of objects in the database such that it covers a set of given keywords collectively and has the smallest cost. Unfortunately, existing exact algorithms have severe scalability problems and existing approximate algorithms, though scalable, cannot guarantee near-to-optimal solutions. In this paper, we study the CoSKQ problem and address the above issues.Firstly, we consider the CoSKQ problem using an existing cost measurement called the maximum sum cost. This problem is called MaxSum-CoSKQ and is known to be NP-hard. We observe that the maximum sum cost of a set of objects is dominated by at most three objects which we call the distance owners of the set. Motivated by this, we propose a distance owner-driven approach which involves two algorithms: one is an exact algorithm which runs faster than the best-known existing algorithm by several orders of magnitude and the other is an approximate algorithm which improves the best-known constant approximation factor from 2 to 1.375.Secondly, we propose a new cost measurement called diameter cost and CoSKQ with this measurement is called Dia-CoSKQ. We prove that Dia-CoSKQ is NP-hard. With the same distance owner-driven approach, we design two algorithms for Dia-CoSKQ: one is an exact algorithm which is efficient and scalable and the other is an approximate algorithm which gives a √3-factor approximation.We conducted extensive experiments on real datasets which verified that the proposed exact algorithms are scalable and the proposed approximate algorithms return near-to-optimal solutions.

/pdf/collective-spatial-keyword-queries-a-distance-owner-driven-27y6mlw5sx.pdf

Collective spatial keyword queries: a distance owner-driven approach

Many web search services allow users to constrain text queries to a geographic location (e.g., yoga classes near Santa Monica). Important examples include local search engines such as Google Local and location-based search services for smart phones. Several research groups have studied the efficient execution of queries mixing text and geography; their approaches usually combine inverted lists with a spatial access method such as an R-tree or space-filling curve. In this paper, we take a fresh look at this problem. We feel that previous work has often focused on the spatial aspect at the expense of performance considerations in text processing, such as inverted index access, compression, and caching. We describe new and existing approaches and discuss their different perspectives. We then compare their performance in extensive experiments on large document collections. Our results indicate that a query processor that combines state-of-the-art text processing techniques with a simple coarse-grained spatial structure can outperform existing approaches by up to two orders of magnitude. In fact, even a naive approach that first uses a simple inverted index and then filters out any documents outside the query range outperforms many previous methods.

/pdf/text-vs-space-efficient-geo-search-query-processing-lesxooh6fc.pdf

Text vs. space: efficient geo-search query processing

Large web search engines use significant hardware and energy resources to process hundreds of millions of queries each day, and a lot of research has focused on how to improve query processing efficiency. One general class of optimizations called early termination techniques is used in all major engines, and essentially involves computing top results without an exhaustive traversal and scoring of all potentially relevant index entries. Recent work in [9,7] proposed several early termination algorithms for disjunctive top-k query processing, based on a new augmented index structure called Block-Max Index that enables aggressive skipping in the index.In this paper, we build on this work by studying new algorithms and optimizations for Block-Max indexes that achieve significant performance gains over the work in [9,7]. We start by implementing and comparing Block-Max oriented algorithms based on the well-known Maxscore and WAND approaches. Then we study how to build better Block-Max index structures and design better index-traversal strategies, resulting in new algorithms that achieve a factor of 2 speed-up over the best results in [9] with acceptable space overheads. We also describe and evaluate a hierarchical algorithm for a new recursive Block-Max index structure.

Optimizing top-k document retrieval strategies for block-max indexes

In this paper we consider the problem of web page usage prediction in a web site by modeling users' navigation history and web page content with weighted suffix trees. This user's navigation prediction can be exploited either in an on-line recommendation system in a web site or in a web page cache system. The method proposed has the advantage that it demands a constant amount of computational effort per one user's action and consumes a relatively small amount of extra memory space. These features make the method ideal for an on-line working environment. Finally, we have performed an evaluation of the proposed scheme with experiments on various web site log files and web pages and we have found that its quality performance is fairly well and in many cases an outperforming one.

A web page usage prediction scheme using sequence indexing and clustering techniques

A large amount of research has focused on faster methods for finding top-k results in large document collections, one of the main scalability challenges for web search engines. In this paper, we propose a method for accelerating such top-k queries that builds on and generalizes methods recently proposed by several groups of researchers based on Block-Max Indexes. In particular, we describe a system that uses a new filtering mechanism, based on a combination of block maxima and bitmaps, that radically reduces the number of documents that have to be further evaluated. Our filtering mechanism exploits the SIMD processing capabilities of current microprocessors, and it is optimized through caching policies that select and store suitable filter structures based on properties of the query load. Our experimental evaluation shows that the mechanism results in very significant speed-ups for disjunctive top-k queries under several state-of-the-art algorithms, including a speed-up of more than a factor of 2 over the fastest previously known methods.

A candidate filtering mechanism for fast top-k query processing on modern cpus

Current search engines use very complex ranking functions based on hundreds of features. While such functions return high-quality results, they create efficiency challenges as it is too costly to fully evaluate them on all documents in the union, or even intersection, of the query terms. To address this issue, search engines use a series of cascading rankers, starting with a very simple ranking function and then applying increasingly complex and expensive ranking functions on smaller and smaller sets of candidate results. Researchers have recently started studying several problems within this framework of query processing by cascading rankers; see, e.g., [5, 13, 17, 51]. We focus on one such problem, the design of the initial cascade. Thus, the goal is to very quickly identify a set of good candidate documents that should be passed to the second and further cascades. Previous work by Asadi and Lin [3, 5] showed that while a top-k computation on either the union or intersection gives good results, a further optimization using a global document ordering based on spam scores leads to a significant reduction in quality. Our contribution is to propose an alternative framework that builds specialized single-term and pairwise index structures, and then during query time selectively accesses these structures based on a cost budget and a set of early termination techniques. Using an end-to-end evaluation with a complex machine-learned ranker, we show that our approach finds candidates about an order of magnitude faster than a conjunctive top-k computation, while essentially matching the quality.

Constantinos Dimopoulos

Papers

Text vs. space: efficient geo-search query processing

Optimizing top-k document retrieval strategies for block-max indexes

A web page usage prediction scheme using sequence indexing and clustering techniques

A candidate filtering mechanism for fast top-k query processing on modern cpus

Fast First-Phase Candidate Generation for Cascading Rankers