Kinds Of Queries Research Articles

Privacy-preserving top-[formula omitted] spatio-temporal keyword preference query

With the rapid advancement of location-based services and mobile devices, spatial keyword query attracts increasing attention. In this paper, we focus on a new query type known as top-k spatio-temporal keyword preference query. This kind of query considers both the spatial object itself and other spatial objects in the neighborhood to return k spatial objects with the highest score. These k spatial objects satisfy spatial and temporal constraints, while their scores are determined by the keyword similarity of the neighboring spatial objects. We propose a scheme to enable privacy-preserving top-k spatio-temporal keyword preference queries. To effectively represent temporal information, we employ time vectors to denote time periods, allowing us to assess whether the data satisfies temporal constraints based on the inner product of time vectors. Furthermore, we adopt a two-step strategy to execute the query. The first step is to find all Points of Interest (POIs) that meet the spatial and temporal constraints. The second step is to calculate the score of each POI and return the top-k POIs with the highest score. To enhance query efficiency, we build a tree index structure that can achieve sub-linear query complexity. Additionally, we utilize EASPE algorithm to encrypt both the index and the query, ensuring privacy-preserving capabilities. Security analysis proves that our scheme satisfies CQA2-security. At the same time, experimental evaluation validates the query performance of our scheme.

Fully Dynamic Contraction Hierarchies with Label Restrictions on Road Networks

In the real world, road networks with weight and label on edges can be applied in several application domains. The shortest path query with label restrictions has been receiving increasing attention recently. To efficiently answer such kind of queries, a novel index, namely Contraction Hierarchies with Label Restrictions (CHLR), is proposed in the literature. However, existing studies mainly focus on the static road networks and do not support the CHLR maintenance when the road networks are dynamically changed. Motivated by this, in this paper, we investigate the CHLR maintenance problem in dynamic road networks. We first devise a baseline approach to update CHLR by recomputing the potential affected shortcuts. However, many shortcuts recomputed in baseline do not change in fact, which leads to unnecessary overhead of the baseline. To overcome the drawbacks of baseline, we further propose a novel CHLR maintenance algorithm which can only travel little shortcuts through an update propagate chain with accuracy guarantee. Moreover, an optimization strategy is presented to further improve the efficiency of index maintenance. Considering the frequency of edge changes, we also propose a batch index maintenance algorithm to handle batch edge changes which can process a large number of edge changes at once. Furthermore, a parallel method is proposed to further accelerate calculations. Extensive and comprehensive experiments are conducted on real road networks. The experimental results demonstrate the efficiency and effectiveness of our proposed algorithms.

Comparative Synthesis: Learning Near-Optimal Network Designs by Query

When managing wide-area networks, network architects must decide how to balance multiple conflicting metrics, and ensure fair allocations to competing traffic while prioritizing critical traffic. The state of practice poses challenges since architects must precisely encode their intent into formal optimization models using abstract notions such as utility functions, and ad-hoc manually tuned knobs. In this paper, we present the first effort to synthesize optimal network designs with indeterminate objectives using an interactive program-synthesis-based approach. We make three contributions. First, we present comparative synthesis, an interactive synthesis framework which produces near-optimal programs (network designs) through two kinds of queries (Validate and Compare), without an objective explicitly given. Second, we develop the first learning algorithm for comparative synthesis in which a voting-guided learner picks the most informative query in each iteration. We present theoretical analysis of the convergence rate of the algorithm. Third, we implemented Net10Q, a system based on our approach, and demonstrate its effectiveness on four real-world network case studies using black-box oracles and simulation experiments, as well as a pilot user study comprising network researchers and practitioners. Both theoretical and experimental results show the promise of our approach.

A scalable and generic approach to range joins

Analytical database systems provide great insights into large datasets and are an excellent tool for data exploration and analysis. A central pillar of query processing is the efficient evaluation of equi-joins, typically with linear-time algorithms (e.g. hash joins). However, for many use-cases with location and temporal data, non-equi joins, like range joins, occur in queries. Without optimizations, this typically results in nested loop evaluation with quadratic complexity. This leads to unacceptable query execution times. Different mitigations have been proposed in the past, like partitioning or sorting the data. While these allow for handling certain classes of queries, they tend to be restricted in the kind of queries they can support. And, perhaps even more importantly, they do not play nice with additional equality predicates that typically occur within a query and that have to be considered, too. In this work, we present a kd-tree-based, multi-dimension range join that supports a very wide range of queries, and that can exploit additional equality constraints. This approach allows us to handle large classes of queries very efficiently, with negligible memory overhead, and it is suitable as a general-purpose solution for range queries in database systems. The join algorithm is fully parallel, both during the build and the probe phase, and scales to large problem instances and high core counts. We demonstrate the feasibility of this approach by integrating it into our database system Umbra and performing extensive experiments with both large real world data sets and with synthetic benchmarks used for sensitivity analysis. In our experiments, it outperforms hand-tuned Spark code and all other database systems that we have tested.

Querying graph databases using context-free grammars

Path queries are used to specify paths inside a data graph to match a given pattern. Query languages such as SPARQL usually include support for regular path patterns defined by means of regular expressions. Context-free path queries define a path whose language can be defined by a context-free grammar. This kind of query is interesting in practice in domains such as genetics, data science, and source code analysis. In this paper, we present an algorithm for context-free path query processing. Our algorithm works by looking for localized paths, allowing us to process subgraphs, in contrast to other approaches that have to process the whole graph. It also takes any context-free grammar as input, avoiding the use of normal forms that can be problematic. The grammar normalization process may introduce a large number of non-terminal symbols and production rules, what, in general, reflects on more runtime and memory consumption by evaluation algorithms. We prove the correctness of our approach and show its runtime and memory complexity. We show the viability of our approach by means of prototypes implemented in Go and Python. We run experiments proposed in recent works, which include both synthetic and real RDF databases, and introduce a more realistic scenario inspired in Biology. Our algorithm shows performance gains when compared to other algorithms implemented using single-thread programs.

A Sketch Framework for Approximate Data Stream Processing in Sliding Windows

Data stream processing has become a hot issue in recent years due to the arrival of big data era. There are three fundamental stream processing tasks: membership query, frequency query and Top-K query. While most existing solutions address these queries in fixed windows, this paper focuses on a more challenging task: answering these queries in sliding windows. While most existing solutions address different kinds of queries by using different algorithms, this paper focuses on a generic framework. In this paper, we propose a generic framework, namely Sliding sketches, which can be applied to many existing solutions for the above three queries, and enable them to support queries in sliding windows. We apply our framework to five state-of-the-art sketches for the above three kinds of queries. Theoretical analysis and extensive experimental results show that after using our framework, the accuracy of existing sketches that do not support sliding windows becomes much higher than the corresponding best prior art. We released all the source code at Github.

Decision Rules Derived from Optimal Decision Trees with Hypotheses.

Conventional decision trees use queries each of which is based on one attribute. In this study, we also examine decision trees that handle additional queries based on hypotheses. This kind of query is similar to the equivalence queries considered in exact learning. Earlier, we designed dynamic programming algorithms for the computation of the minimum depth and the minimum number of internal nodes in decision trees that have hypotheses. Modification of these algorithms considered in the present paper permits us to build decision trees with hypotheses that are optimal relative to the depth or relative to the number of the internal nodes. We compare the length and coverage of decision rules extracted from optimal decision trees with hypotheses and decision rules extracted from optimal conventional decision trees to choose the ones that are preferable as a tool for the representation of information. To this end, we conduct computer experiments on various decision tables from the UCI Machine Learning Repository. In addition, we also consider decision tables for randomly generated Boolean functions. The collected results show that the decision rules derived from decision trees with hypotheses in many cases are better than the rules extracted from conventional decision trees.

Querying RDF Databases with Sub-CONSTRUCTs

Graph query languages feature mainly two kinds of queries when applied to a graph database: those inspired by relational databases which return tables such as SELECT queries and those which return graphs such as CONSTRUCT queries in SPARQL. The latter are object of study in the present paper. For this purpose, a core graph query language GrAL is defined with focus on CONSTRUCT queries. Queries in GrAL form the final step of a recursive process involving so-called GrAL patterns. By evaluating a query over a graph one gets a graph, while by evaluating a pattern over a graph one gets a set of matches which involves both a graph and a table. CONSTRUCT queries are based on CONSTRUCT patterns, and sub-CONSTRUCT patterns come for free from the recursive definition of patterns. The semantics of GrAL is based on RDF graphs with a slight modification which consists in accepting isolated nodes. Such an extension of RDF graphs eases the definition of the evaluation semantics, which is mainly captured by a unique operation called Merge. Besides, we define aggregations as part of GrAL expressions, which leads to an original local processing of aggregations.

An assortment of Query Based Summarization technique (QBS) – A Study

This paper presents latest techniques followed in Query based single and multi-document summarization. In this era, we see an exponential growth of text data in all fields, it is rigid to get vital and precise information on time. Automatic Text Summarization is obligatory to reduce the stint to analyses elongated and complicated document. Text summarization is categorized based on many factors, each has its own merits and demerits. Query based summarization has attracted increasing interest due to its usage in many applications. This survey includes query-based summarization using graph, hyper graph, RAKE algorithm and Linguistic knowledge. DUC is the popular data set used in most text summarization, but due to its inability to suit various kinds of queries, some authors have used their own data set.

Tasso in Music Project (TiMP): Digital Edition of the Settings of Torquato Tasso’s Poetry, c. 1570–1640

Review| April 01 2021 Tasso in Music Project (TiMP): Digital Edition of the Settings of Torquato Tasso’s Poetry, c. 1570–1640 Tasso in Music Project (TiMP): Digital Edition of the Settings of Torquato Tasso’s Poetry, c. 1570–1640. Emiliano Ricciardi, Director and General Editor; Craig Stuart Sapp, Technical Director. URL: https://www.tassomusic.org/ Lucia Marchi Lucia Marchi LUCIA MARCHI is adjunct faculty in the Modern Languages Department and the School of Music at DePaul University in Chicago. She is a specialist in late medieval music and the Italian madrigal. Her work has appeared in Recercare, Acta musicologica, Essays in Medieval Studies, and Studi musicali. Her critical edition of Luca Marenzio’s Fourth Book of Madrigals for Six Voices is forthcoming on MODE (www.marenzio.org). She is a member of the Musicology at Kalamazoo program committee. Search for other works by this author on: This Site PubMed Google Scholar Journal of the American Musicological Society (2021) 74 (1): 187–195. https://doi.org/10.1525/jams.2021.74.1.187 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Lucia Marchi; Tasso in Music Project (TiMP): Digital Edition of the Settings of Torquato Tasso’s Poetry, c. 1570–1640. Journal of the American Musicological Society 1 April 2021; 74 (1): 187–195. doi: https://doi.org/10.1525/jams.2021.74.1.187 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search nav search search input Search input auto suggest search filter All ContentJournal of the American Musicological Society Search Databases change the way we do research, both because of the quantity of information they make available, and because of the unexpected paths along which they guide and redirect our original intentions. I am not suggesting that serendipitous discoveries are not afforded by traditional methods of research—one might, for example, browse the shelves of a library—but simply that, in the computer-assisted humanities, such a process is implicit in every kind of query. Up to now, scholars interested in assessing the considerable impact of the Italian poet Torquato Tasso (1544–95) on the madrigal repertoire have had to rely on the indices of the so-called “Nuovo Vogel,”1 on RISM (series A/I and B/I), or on a list compiled by Antonio Vassalli in the “classic” Tasso, la musica, i musicisti of 1988.2 One would consult a traditional catalog of this kind with a specific question in mind (for example, Which composers... You do not currently have access to this content.

Graph cells: Top-k structural-textual aggregated query over information networks

The graph OLAP aggregated analysis in information networks has been extensively studied. However, previous works have neglected to integrate the structural information into this kind of query and ignored the influence of enough textual information in graph aggregation operations. In this paper, we propose a novel OLAP query called top-k structural-textual aggregated graph cell query to analyze the information data. According to the given keywords, this query is to find top-k structural-textual aggregated graph cells in text-rich multidimensional information networks. Under the conditions of matching attribution values in a portion of dimensions, a graph cell is defined as a subgraph of the network. It only contains documents of all included vertices in this subgraph. To distinguish the importance of different graph cells, we firstly design a dominating number-based threshold testing and a flexible ranking function integrating the text similarity with the query and the structural size to obtain k most relevant graph cells. Then, we propose a new hybrid index structure and a filtering-and-verification framework, which includes an efficient search algorithm and several pruning and bounding techniques. Finally, we verify the effectiveness and efficiency of the proposed methods through extensive experiments.

Performance Art After The 1990s

Abstract Performance art has become over-represented in contemporary art museums, at art fairs, at major international exhibitions. In this context, I have proposed a brief overview of the history of performance in North America and Europe, to identify conceptual variations or continuities in post-1989 performing arts practices. What kind of queries caused the resort to the body? Which of the criticisms are still current and which new issues are formulated in the present geopolitical framework or in particular socio-political contexts? In order to answer these questions, I’ve selected a number of national and international male/ female artists whose practices illustrate the main directions in today’s performance art.

Кэширование машинного кода в динамическом компиляторе SQL-запросов для СУБД PostgreSQL

As the efficiency of main and external memory grows, alongside with decreasing hardware costs, the performance of database management systems (DBMS) on certain kinds of queries is more determined by CPU characteristics and the way it is utilized. Relational DBMS utilize diverse execution models to run SQL queries. Those models have different properties, but in either way suffer from substantial overhead during query plan interpretation. The overhead comes from indirect calls to handler functions, runtime checks and large number of branch instructions. One way to solve this problem is dynamic query compilation that is reasonable only in those cases when query interpretation time is larger than the time of compilation and optimized machine code execution. This requirement can be satisfied only when the amount of data to be processed is large enough. If query interpretation takes milliseconds to finish, then the cost of dynamic compilation can be hundreds of times more than the execution time of generated machine code. To pay off the cost of dynamic compilation, the generated machine code has to be reused in subsequent executions, thus saving the cost of code compilation and optimization. In this paper, we examine the method of machine code caching in our query JIT-compiler for DBMS PostgreSQL. The proposed method allows us to eliminate compilation overhead. The results show that dynamic compilation of queries with machine code caching feature gives a significant speedup on OLTP queries.

SGPM: a privacy protected approach of time-constrained graph pattern matching in cloud

Graph pattern matching (GPM) is an important operation on graph computation. Most existing work assumes that query graph or data graph is static, which is contrary to the fact that graphs in real life are intrinsically dynamic. Therefore, time-constrained graph pattern matching has been introduced. However, querying time-constrained graph pattern in a temporal graph is not an easy work because of the high computation complexity. Query outsourcing, with the help of cloud computing, is adopted in this paper. Outsourcing of data storage and query becomes increasingly popular due to the prevalence of cloud computing. However, sensitive data need to be encrypted before outsourcing for various privacy concerns. To execute queries over encrypted data is a very challenging problem and has received much attention recently. However, most existing approaches only support limited kinds of queries, moreover, they cannot be completely outsourced. In this paper, a Somewhat Homomorphic Encryption ($\mathcal {S}{\mathscr{H}}\mathcal {E}$) approach is adopted to design a protocol which enables general queries on encrypted data and query outsourcing as well. The key issue in the paper is to (1) enable the query provider to filter data rows with homomorphic encrypted result of comparison operators; (2) completely relieve the data owner of partaking of the process of queries. An effective dualcloud protocol (DCP) which enables the cloud to understand the result of comparisons though homomorphic encrypted values is proposed. Moreover, The efficiency of the baseline approach is greatly improved by packing and GPU-accelerating technologies. Experimental study shows that the optimized approach outperforms the baseline approach and the recently reported similar approaches.

Technical Perspective for

The main processing paradigm in data management is bulk processing. As introduced by Codd in the early 70's, under this paradigm relations are processed in bulk, one operator at a time. When applied to relations, this paradigm leads to relational algebra, and its variants, relational calculus, and SQL. Over the years, data management was faced with the challenge of extending bulk processing operators to new kinds of data, and/or new kinds of queries: nested relations, semistructured data, recursive queries. Each such extension requires significant systems development, which should be accompanied, in fact preceded, by a careful study of the expressive power of the new language. Is it as expressive, more expressive, or less expressive than relational algebra? The answer to this question has profound implications on the ability of data processing engines to optimize, compute, distribute, reuse queries in that language. For example, extending relational algebra with nested relations does not increase its expressive power, while extending it with fixpoint does, explaining why modern query engines have an easier time supporting JSON than recursion.

Querying XML documents using Prolog engines: When is this a good idea?

XML has become a universal standard for information exchange over the Web due to features such as simple syntax and extensibility. Processing queries over these documents has been the focus of several research groups. In fact, there is broad literature in efficient XML query processing which explore indexes, fragmentation techniques, etc. However, for answering complex queries, existing approaches mainly analyze information that is explicitly defined in the XML document. A few work investigate the use of Prolog to increase the query possibilities, allowing inference over the data content. This can cause a significant increase in the query possibilities and expressive power, allowing access to non-obvious information. However, this requires translating the XML documents into Prolog facts. But for regular queries (which do not require inference), is this a good alternative? What kind of queries could benefit from the Prolog translation? Can we always use Prolog engines to execute XML queries in an efficient way? There are many questions involved in adopting an alternative approach to run XML queries. In this work, we investigate this matter by translating XML queries into Prolog queries and comparing the query processing times using Prolog and native XML engines. Our work contributes by providing a set of heuristics that helps users to decide when to use Prolog engines to process a given XML query. In summary, our results show that queries that search elements by a key value or by its position (simple search) are more efficient when run in Prolog than in native XML engines. Also, queries over large datasets, or that searches for substrings perform better when run by native XML engines.

Domain Partitioning: Approach to Computing Average Iceberg Queries

: Data analytics and data mining systems work on data which stored in files, the files are not store relationships among the data, from such kind of data we compute aggregate values over the set of required attributes for find insights of data, find attributes values which aggregation values greater than threshold such kind of queries called iceberg queries. Computing iceberg queries with average aggregate function is default, because limited memory available. Existing method are suffers with re-computation of candidate. We proposed a Record Traction Algorithm(RTA) ,it use Domain partitioning approach, it avoid re-computation of candidate in during next scan of data set, it use bit vector and bitmap numbers for Domain Partitioning the data, our experiment reveals that our approach generate a candidate only once and input data will reduced in further candidate sets.

O2iJoin: An Efficient Index-Based Algorithm for Overlap Interval Join

Time intervals are often associated with tuples to represent their valid time in temporal relations, where overlap join is crucial for various kinds of queries. Many existing overlap join algorithms use indices based on tree structures such as quad-tree, B+-tree and interval tree. These algorithms usually have high CPU cost since deep path traversals are unavoidable, which makes them not so competitive as data-partition or plane-sweep based algorithms. This paper proposes an efficient overlap join algorithm based on a new two-layer flat index named as Overlap Interval Inverted Index (i.e., O2i Index). It uses an array to record the end points of intervals and approximates the nesting structures of intervals via two functions in the first layer, and the second layer uses inverted lists to trace all intervals satisfying the approximated nesting structures. With the help of the new index, the join algorithm only visits the must-be-scanned lists and skips all others. Analyses and experiments on both real and synthetic datasets show that the proposed algorithm is as competitive as the state-of-the-art algorithms.

Route Searching using Modified k-Nearest Neighbor with Hill Climbing over Trajectories

Optimal planning for public transportation is one of the keys to sustainable development and better quality of life in urban areas. Based on mobility patterns, propose a localized transportation mode choice model, with which we can dynamically predict the bus travel demand for different bus routing. This model is then used for bus routing optimization which aims to convert as many people from private transportation to public transportation as possible given budget constraints on the bus route modification. It also leverages the model to identify region pairs with flawed bus routes, which are effectively optimized using our approach. To validate the effectiveness of the proposed methods, extensive studies are performed on real world data collected in Beijing which contains 19 million taxi trips and 10 million bus trips. GPS enables mobile devices to continuously provide new opportunities to improve our daily lives. For example, the data collected in applications created by Ola, Uber or Public Transport Authorities can be used to plan transportation routes, estimate capacities, and proactively identify low coverage areas. Now, study a new kind of query – Modified k-Nearest Neighbor Search with Hill Climbing (MkNNHC), which can be used for route planning and capacity estimation. Given a set of existing routes DR, a set of passenger transitions DT, and a query route Q, an MkNNHC query returns all transitions that take Q as one of its k nearest travel routes. To solve the problem, we first develop an index to handle dynamic trajectory updates, so that the most up-to-date transition data are available for answering an RkNNT query. Then introduce a filter refinement framework for processing MkNNHC queries using the proposed indexes. Experiments on real datasets demonstrate the efficiency and scalability of our approaches.

Hypergraph Optimization for Salient Region Detection Based on Foreground and Background Queries

Graph-based methods have been widely adopted to detect salient objects in images. However, there are two limitations of these methods. First, only one kind of query is employed for saliency propagation on the graph. Second, these methods only represent pairwise relations between vertices and thus give an incomplete representation of the relationships between image regions. In this paper, we propose a foreground- and background-queries-based hypergraph optimization framework for salient region detection. In this framework, both foreground queries and background queries are explicitly exploited to uniformly highlight the salient foreground and suppress the non-salient background. Furthermore, to include both the pairwise and the higher-order relations among two or more vertices, a probabilistic hypergraph is constructed based on local spatial correlation, global spatial correlation, and color correlation to represent the relations among image regions from different views. Extensive experimental results demonstrate the effectiveness of the proposed framework.