k-nearest neighbor (k-NN) search aims at finding k points nearest to a query point in a given dataset. k-NN search is important in various applications, but it becomes extremely expensive in a high-dimensional large dataset. To address this performance issue, locality-sensitive hashing (LSH) is suggested as a method of probabilistic dimension reduction while preserving the relative distances between points. However, the performance of existing LSH schemes is still inconsistent, requiring a large amount of search time in some datasets while the k-NN approximation accuracy is low. In this paper, we target on improving the performance of k-NN search and achieving a consistent k-NN search that performs well in various datasets. In this regard, we propose a novel LSH scheme called Signature Selection LSH (S2LSH). First, we generate a highly diversified signature pool containing signature regions of various sizes and shapes. Then, for a given query point, we rank signature regions of the query and select points in the highly ranked signature regions as k-NN candidates of the query. Extensive experiments show that our approach consistently outperforms the state-of-the-art LSH schemes.

Users are increasingly pursuing complex task-oriented goals on the web, such as making travel arrangements, managing finances, or planning purchases. To this end, they usually break down the tasks into a few codependent steps and issue multiple queries around these steps repeatedly over long periods of time. To better support users in their long-term information quests on the web, search engines keep track of their queries and clicks while searching online. In this paper, we study the problem of organizing a user's historical queries into groups in a dynamic and automated fashion. Automatically identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. In our approach, we go beyond approaches that rely on textual similarity or time thresholds, and we propose a more robust approach that leverages search query logs. We experimentally study the performance of different techniques, and showcase their potential, especially when combined together.

Dynamic authority-based keyword search algorithms, such as ObjectRank and personalized PageRank, leverage semantic link information to provide high quality, high recall search in databases, and the Web. Conceptually, these algorithms require a query-time PageRank-style iterative computation over the full graph. This computation is too expensive for large graphs, and not feasible at query time. Alternatively, building an index of precomputed results for some or all keywords involves very expensive preprocessing. We introduce BinRank, a system that approximates ObjectRank results by utilizing a hybrid approach inspired by materialized views in traditional query processing. We materialize a number of relatively small subsets of the data graph in such a way that any keyword query can be answered by running ObjectRank on only one of the subgraphs. BinRank generates the subgraphs by partitioning all the terms in the corpus based on their co-occurrence, executing ObjectRank for each partition using the terms to generate a set of random walk starting points, and keeping only those objects that receive non-negligible scores. The intuition is that a subgraph that contains all objects and links relevant to a set of related terms should have all the information needed to rank objects with respect to one of these terms. We demonstrate that BinRank can achieve subsecond query execution time on the English Wikipedia data set, while producing high-quality search results that closely approximate the results of ObjectRank on the original graph. The Wikipedia link graph contains about 10^8 edges, which is at least two orders of magnitude larger than what prior state of the art dynamic authority-based search systems have been able to demonstrate. Our experimental evaluation investigates the trade-off between query execution time, quality of the results, and storage requirements of BinRank.

Dynamic authority-based keyword search algorithms, such as ObjectRank and personalized PageRank, leverage semantic link information to provide high quality, high recall search in databases and the Web. Conceptually, these algorithms require a query-time PageRank-style iterative computation over the full graph. This computation is too expensive for large graphs, and not feasible at query time. Alternatively, building an index of pre-computed results for some or all keywords involves very expensive preprocessing. We introduce BinRank, a system that approximates ObjectRank results by utilizing a hybrid approach inspired by materialized views in traditional query processing. We materialize a number of relatively small subsets of the data graph in such a way that any keyword query can be answered by running ObjectRank on only one of the sub-graphs. BinRank generates the sub-graphs by partitioning all the terms in the corpus based on their co-occurrence, executing ObjectRank for each partition using the terms to generate a set of random walk starting points, and keeping only those objects that receive non-negligible scores. The intuition is that a sub-graph that contains all objects and links relevant to a set of related terms should have all the information needed to rank objects with respect to one of these terms. We demonstrate that BinRank can achieve sub-second query execution time on the English Wikipedia dataset, while producing high quality search results that closely approximate the results of ObjectRank on the original graph. The Wikipedia link graph contains about 108edges, which is at least two orders of magnitude larger than what prior state of the art dynamic authority-based search systems have been able to demonstrate. Our experimental evaluation investigates the trade-off between query execution time, quality of the results, and storage requirements of BinRank.

DBPubs is a system for effectively analyzing and exploring the content of database publications by combining keyword search with OLAP-style aggregations, navigation, and reporting. DBPubs starts with keyword search over the content of publications. The publications' metadata such as title, authors, venues, year, and so on, provide traditional OLAP static dimensions, which are combined with dynamic dimensions discovered from the content of the publications in the search result, such as frequent phrases, relevant phrases, and topics. We compute publication ranks based on the link structure between documents, i.e., citations, and aggregate them to find seminal papers, discover trends, and rank authors. We deploy an OLAP tool for multidimensional content exploration through traditional OLAP rollup-drilldown operations on the static and dynamic dimensions, solutions for multi-cube analysis, dynamic navigation of the content, and highlighting of interesting dices of the multidimensional content dataspace.

Our system applies authority-based ranking to keyword search in databases modeled as labeled graphs. Three ranking factors are used: the relevance to the query, the specificity and the importance of the result. All factors are handled using authority-flow techniques that exploit the link-structure of the data graph, in contrast to traditional Information Retrieval. We address the performance challenges in computing the authority flows in databases by using precomputation and exploiting the database schema if present. We conducted user surveys and performance experiments on multiple real and synthetic datasets, to assess the semantic meaningfulness and performance of our system.

We model heterogeneous data sources with cross references, such as those crawled on the (enterprise) web, as a labeled graph with data objects as typed nodes and references or links as edges. Given the labeled data graph, we introduce flexible and efficient querying capabilities that go beyond existing capabilities by additionally discovering meaningful relationships between objects that satisfy keyword and/or structured query filters. We introduce the relationship search operator that exploits the link structure between data objects to rank objects related to the result of a filter. We implement the relationship search operator using the ObjectRank [1] algorithm that uses the random surfer model. We study several alternatives for constructing summary graphs for query results that consist of individual and aggregate nodes that are somehow linked to qualifying result nodes. Some of the summary graphs are useful for presenting query results to the user, while others could be used to evaluate subsequent queries efficiently without considering all the nodes and links in the original data graph. ACM CLASSIFICATOIN

We present ObjectRank demo system that performs authority-based keyword search on bibliographic databases. We also provide Inverse ObjectRank as a keyword-specific specificity metric and other calibration parameters such as Global ObjectRank. Users can specify various combinations of calibration values to control the behavior of the demo. Finally, we propose a methodology that enables us to extend query results using the ontology graph.