Approach For Queries Research Articles

Visual Ontology-Based Query Approach for Data Access in Heterogeneous Expertise Environment: Application in PLM Biomedical Imaging

Computer-Aided Design and Applications is an international journal on the applications of CAD and CAM. It publishes papers in the general domain of CAD plus in emerging fields like bio-CAD, nano-CAD, soft-CAD, garment-CAD, PLM, PDM, CAD data mining, CAD and the internet, CAD education, genetic algorithms and CAD engines. The journal is aimed at all developers and users of CAD technology to ptovide CAD solutions for various stages of design and manufacturing. The journal publishes all about Computer-Aided Design and Computer-Aided technologies.

Efficient Evaluation of Multi-Column Selection Predicates in Main-Memory

Efficient evaluation of selection predicates is a performance-critical task, for instance to reduce intermediate result sizes being the input for further operations. With analytical queries getting more and more complex, the number of evaluated selection predicates per query and table rises, too. This leads to numerous multi-column selection predicates. Recent approaches to increase the performance of main-memory databases for selection-predicate evaluation aim at optimally exploiting the speed of the CPU by using accelerated scans. However, scanning each column one by one leaves tuning opportunities open that arise if all predicates are considered together. To this end, we introduce Elf, an index structure that is able to exploit the relation between several selection predicates. Elf features cache sensitivity, an optimized storage layout, fixed search paths, and slight data compression. In a large-scale evaluation, we compare its query performance to state-of-the-art approaches and a sequential scan using SIMD capabilities. Our results indicate a clear superiority of our approach for queries returning less than 10 percent of all tuples – a selectivity almost one order of magnitude larger than observed for related indexing approaches. For TPC-H queries with multi-column selection predicates, we achieve a speedup between factor five and two orders of magnitude, mainly depending on the selectivity of the predicates. Further scaling experiments reveal that for large data sets, these speedup factors are expected to increase, due to more densely populated data spaces. Finally, our results indicate that using a delta-store like concept to support periodic insertions results in virtually no performance penalty for reasonable sizes of a write-optimized Elf as delta store.

Answering range queries under local differential privacy

Counting the fraction of a population having an input within a specified interval i.e. a range query, is a fundamental data analysis primitive. Range queries can also be used to compute other core statistics such as quantiles, and to build prediction models. However, frequently the data is subject to privacy concerns when it is drawn from individuals, and relates for example to their financial, health, religious or political status. In this paper, we introduce and analyze methods to support range queries under the local variant of differential privacy [23], an emerging standard for privacy-preserving data analysis. The local model requires that each user releases a noisy view of her private data under a privacy guarantee. While many works address the problem of range queries in the trusted aggregator setting, this problem has not been addressed specifically under untrusted aggregation (local DP) model even though many primitives have been developed recently for estimating a discrete distribution. We describe and analyze two classes of approaches for range queries, based on hierarchical histograms and the Haar wavelet transform. We show that both have strong theoretical accuracy guarantees on variance. In practice, both methods are fast and require minimal computation and communication resources. Our experiments show that the wavelet approach is most accurate in high privacy settings, while the hierarchical approach dominates for weaker privacy requirements.

A novel privacy- and integrity-preserving approach for multidimensional data range queries in two-tiered wireless sensor networks

To address the problem of storage nodes in wireless sensor networks being vulnerable to attack, this article proposes a new range query protocol, WQuery, based on multidimensional data privacy and integrity protection. To ensure the privacy of multidimensional data, the WQuery protocol encodes multidimensional sensing data and user query requests based on a bucket-partitioning scheme and sequential encryption mechanism at the sensor node and sink node. Therefore, the storage nodes cannot sense the real value of sensing data and query requests under the premise of correctly executing the request. To ensure the integrity of the multidimensional data query results, WQuery includes an integrity verification scheme based on the new tree-to-destination data structure. The scheme translates the actual query scope of a user request into a matched set of bucket labels and verifies the integrity of query results through the tree-to-destination data structure. Simulation results show that the tree-to-destination scheme is flexible, easy to implement, and highly secure, thereby effectively reducing energy consumption.

An Efficient Indexing Approach for Continuous Spatial Approximate Keyword Queries over Geo-Textual Streaming Data

Current social-network-based and location-based-service applications need to handle continuous spatial approximate keyword queries over geo-textual streaming data of high density. The continuous query is a well-known expensive operation. The optimization of continuous query processing is still an open issue. For geo-textual streaming data, the performance issue is more serious since both location information and textual description need to be matched for each incoming streaming data tuple. The state-of-the-art continuous spatial-keyword query indexing approaches generally lack both support for approximate keyword matching and high-performance processing for geo-textual streaming data. Aiming to tackle this problem, this paper first proposes an indexing approach for efficient supporting of continuous spatial approximate keyword queries by integrating m i n - w i s e signatures into an AP-tree, namely AP-tree + . AP-tree + utilizes the one-permutation m i n - w i s e hashing method to achieve a much lower signature maintenance costs compared with the traditional m i n - w i s e hashing method because it only employs one hashing function instead of dozens. Towards providing a more efficient indexing approach, this paper has explored the feasibility of parallelizing AP-tree + by employing a Graphic Processing Unit (GPU). We mapped the AP-tree + data structure into the GPU’s memory with a variety of one-dimensional arrays to form the GPU-aided AP-tree + . Furthermore, a m i n - w i s e parallel hashing algorithm with a scheme of data parallel and a GPU-CPU data communication method based on a four-stage pipeline way have been used to optimize the performance of the GPU-aided AP-tree + . The experimental results indicate that (1) AP-tree + can reduce the space cost by about 11% compared with MHR-tree, (2) AP-tree + can hold a comparable recall and 5.64× query performance gain compared with MHR-tree while saving 41.66% maintenance cost on average, (3) the GPU-aided AP-tree + can attain an average speedup of 5.76× compared to AP-tree + , and (4) the GPU-CPU data communication scheme can further improve the query performance of the GPU-aided AP-tree + by 39.4%.

A novel approach for query over encrypted data in database

Database management is considered an essential component of many information systems to store data. Some database system contains secure data; these data are protected by using encryption techniques. The query performance is affected by encryption techniques. Therefore; should be a balance between the security and performance. A new technique in this research work has been proposed to enhance the query performance over the encrypted fields in a database system. This technique is based on producing a unique hash value for each secure data and transforms the SQL query into an appropriate formula to be executed over the hash value fields. The proposed approach has eliminated any statistical relationship between encrypted and hash value fields. The time of execution encryption/decryption is reduced to enhance the performance of the query over encrypted secure data in the proposed technique. A set of experiments are carried out, and the results indicate that the performance of SQL-query is enhanced by reducing the average response time to 14 compared with others related approaches.

A Novel Approach for Query over Encrypted Data in Database

Database management is considered an essential component of many information systems to store data. Some database system contains secure data; these data are protected by using encryption techniques. The query performance is affected by encryption techniques. Therefore; should be a balance between the security and performance. A new technique in this research work has been proposed to enhance the query performance over the encrypted fields in a database system. This technique is based on producing a unique hash value for each secure data and transforms the SQL query into an appropriate formula to be executed over the hash value fields. The proposed approach has eliminated any statistical relationship between encrypted and hash value fields. The time of execution encryption/decryption is reduced to enhance the performance of the query over encrypted secure data in the proposed technique. A set of experiments are carried out, and the results indicate that the performance of SQL-query is enhanced by reducing the average response time to 14 compared with others related approaches.

A Cause-Based Classification Approach for Malicious DNS Queries Detected Through Blacklists

Some of the most serious security threats facing computer networks involve malware. To prevent this threat, administrators need to swiftly remove the infected machines from their networks. One common way to detect infected machines in a network is by monitoring communications based on blacklists. However, detection using this method has the following two problems: no blacklist is completely reliable, and blacklists do not provide sufficient evidence to allow administrators to determine the validity and accuracy of the detection results. Therefore, simply matching communications with blacklist entries is insufficient, and administrators should pursue their detection causes by investigating the communications themselves. In this paper, we propose an approach for classifying malicious DNS queries detected through blacklists by their causes. This approach is motivated by the following observation: a malware communication is divided into several transactions, each of which generates queries related to the malware; thus, surrounding queries that occur before and after a malicious query detected through blacklists help in estimating the cause of the malicious query. Our cause-based classification drastically reduces the number of malicious queries to be investigated because the investigation scope is limited to only representative queries in the classification results. In experiments, we have confirmed that our approach could group 388 malicious queries into 3 clusters, each consisting of queries with a common cause. These results indicate that administrators can briefly pursue all the causes by investigating only representative queries of each cluster, and thereby swiftly address the problem of infected machines in the network.

An Efficient Top-$K$ Spatial Keyword Typicality and Semantic Query

Existing spatial keyword query processing models mainly consider the spatial proximity and text relevancy between spatial objects and spatial keyword query, which usually makes the top- k answer objects are similar to each other. However, the user hopes to obtain the top- k results that are typical and semantically related to his/her query intention. This paper proposes a top- k spatial keyword typicality and sematic querying approach which can expeditiously provide top- k typical and semantically related objects to the given query. The approach consists of two processing steps. During the offline step, we first analyze the location-semantic relationships between spatial objects by considering both the location similarity and document semantic relevancy between them. For measuring the semantic similarity between documents associated to the spatial objects, we propose two methods, the keyword coupling relationship-based document similarity measure and the Word2Vec-CNN-based document similarity measure. Then, the Gaussian probabilistic density-based estimation method is leveraged to find a few representative objects from the dataset and then the order/permutation of remaining objects in the dataset can be generated corresponding to each representative object. The objects in the permutation are ranked in descending order according to their location-semantic relationships to the representative object. When a spatial keyword query coming, the online processing step first computes the spatial proximity and semantic relevancy between the query and each representative object, and then a small number of orders generated in the offline step can be selected and used at querying time to facilitate top- k typical and semantically related object selection by using the threshold algorithm (TA). Results of a preliminary user study demonstrate our location-semantic relationship measuring method can capture the location similarity and semantic relevancy between spatial objects accurately. The efficiency of typicality analysis and TA-based top- k selection algorithm is also demonstrated.

A Natural-language-based Visual Query Approach of Uncertain Human Trajectories.

Visual querying is essential for interactively exploring massive trajectory data. However, the data uncertainty imposes profound challenges to fulfill advanced analytics requirements. On the one hand, many underlying data does not contain accurate geographic coordinates, e.g., positions of a mobile phone only refer to the regions (i.e., mobile cell stations) in which it resides, instead of accurate GPS coordinates. On the other hand, domain experts and general users prefer a natural way, such as using a natural language sentence, to access and analyze massive movement data. In this paper, we propose a visual analytics approach that can extract spatial-temporal constraints from a textual sentence and support an effective query method over uncertain mobile trajectory data. It is built up on encoding massive, spatially uncertain trajectories by the semantic information of the POls and regions covered by them, and then storing the trajectory documents in text database with an effective indexing scheme. The visual interface facilitates query condition specification, situation-aware visualization, and semantic exploration of large trajectory data. Usage scenarios on real-world human mobility datasets demonstrate the effectiveness of our approach.

Trust-Aware Personalized Route Query Using Extreme Learning Machine in Location-Based Social Networks

The task of personalized route query is to find the optimal trip that contains the keywords specified by the query user and satisfies the travel distance constraints. The previous studies mostly focus on collaborative filtering by considering user similarity. Trust is one of the most important factors in decision-making that has been neglected by the existing studies of personalized route query. In this paper, we propose a new type of personalized route query by incorporating trust. We propose a social trust-based optimal trip selection (STOTS) framework for personalized route query. STOTS consists of three key components. The first component predicts social trust based on extreme learning machine (ELM), denoted STP-ELM, that exploits social information and user behavioral patterns as features. In the second component, we propose a novel model to incorporate social trust into personalized route query. Additionally, we propose an index to speed up query processing. In the third component, we propose an optimal route query algorithm called RouteHunter that aims to find an appropriate route satisfying the user-specified constraints. The experiment results show that (1) our social trust prediction approach based on ELM attains superior regression efficiency compared to other traditional methods; (2) our proposed index can efficiently accelerate personalized route query processing; and (3) our route query approach can achieve a better performance than the baseline approach. This paper studies a novel personalized route query incorporating social trust in location-based social networks. We propose a social trust-based optimal trip selection (STOTS) framework that uses ELM to evaluate social trust, applies a ranking model to incorporate social trust, and includes an algorithm to find the required route. Experimental results encouragingly demonstrate the efficiency and effectiveness of our proposed approach.

SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays.

Details-on-demand is a crucial feature in the visual information-seeking process but is often only implemented in highly constrained settings. The most common solution, hover queries (i.e., tooltips), are fast and expressive but are usually limited to single mark (e.g., a bar in a bar chart). 'Queries' to retrieve details for more complex sets of objects (e.g., comparisons between pairs of elements, averages across multiple items, trend lines, etc.) are difficult for end-users to invoke explicitly. Further, the output of these queries require complex annotations and overlays which need to be displayed and dismissed on demand to avoid clutter. In this work we introduce SmartCues, a library to support details-on-demand through dynamically computed overlays. For end-users, SmartCues provides multitouch interactions to construct complex queries for a variety of details. For designers, SmartCues offers an interaction library that can be used out-of-the-box, and can be extended for new charts and detail types. We demonstrate how SmartCues can be implemented across a wide array of visualization types and, through a lab study, show that end users can effectively use SmartCues.

An intelligent query approach by integrating visual queries with full-text search

This research focuses on a combined query approach for handling structured and unstructured data. A Visual Query System (VQS) is usually developed for handling structured data. It enables end users to formulate queries through visual representations without the need of having knowledge of database and query languages. As an important branch of today's information retrieval technology, Full-Text Search (FTS) is an important approach for dealing with unstructured data. Hence, a hybrid system can be developed, which integrates a Visual Query System with a Full-Text Search engine and which acts as an intelligent search interface. A customized ranking algorithm can rank the results according to their relevance. This hybrid approach converts the natural language text queries and visual queries into a database query language, such as SQL. It can be adopted for different examples of data with a combination of structured and unstructured types to provide an efficient query interface solution. In particular, it can be integrated in web search engines to integrate structured data with unstructured data in the search interface.

An Efficient Optimization Approach for Top-k Queries on Uncertain Data

Uncertain data is inherent in various important applications and Top-[Formula: see text] query on uncertain data is an important query type for many applications. To tackle the performance issue of evaluating Top-[Formula: see text] query on uncertain data, an efficient optimization approach was proposed in this paper. This method can anticipate the tuples most likely to become Top-[Formula: see text] result based on dominant relationship analysis, greatly reducing the amount of data in query processing. When the database is updated, this method could determine whether the change affects the current query result, and help us to avoid unnecessary re-query. The experimental results prove the feasibility and effectiveness of this method.

Probabilistic Time-Constrained Paths Search over Uncertain Road Networks

With the proliferation of positioning technologies and GPS-enabled mobile devices, it has become very important to search for optimal paths to cover required points of interest (POIs, e.g., banks and restaurants) over road networks in many location-based services. In practice, however, traffic conditions are inherently uncertain and dynamically changing over time, which makes it rather challenging to provide accurate results for path queries based on travelling time. Inspired by this, we consider the practical settings of road networks and model them by uncertain road networks (URNs) on which the travelling time of each road is uncertain and captured by a set of travelling time samples. Then, we formalize the probabilistic time-constrained path ( PTP ) query over uncertain road networks to retrieve those paths that not only cover required POIs with constrained service time but also have the minimum travelling times in high confidence. We prove that PTP query problem is NP-hard. In order to answer PTP queries efficiently, we propose an efficient PTP query approach with effective pruning strategies regarding the time constraints on POIs and the probabilistic/rank requirements of queries. Extensive experiments on real road networks validate the efficiency and effectiveness of our PTP query approach.

Understanding demographic and socioeconomic biases of geotagged Twitter users at the county level

ABSTRACTMassive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic biases. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the United States, we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties.

Multi-Query Optimization on RSS Feeds

RSS feeds are text-content rich, semantically heterogeneous, and contain dynamic XML elements streamed in asynchronous and pull strategies. Hence, for efficient retrieval of RSS feeds, semantic-aware querying operators have been proposed in the literature (Getahun and Chbeir in Inf Sci 237(237):313–342, 2013). However, it is commonly admitted that the use of semantic information would improve, on one hand, the relevance of query result but, on the other hand, at the cost of degrading the efficiency and the performance of the system. To benefit from query execution on semantic information while keeping the efficiency of the system, we propose here a multi-query optimization approach for semantic RSS feed queries. Our approach processes queries by examining the semantic relationship between them and their corresponding windows. It generates a multi-query chain for queries using their window relations for faster execution at runtime. In addition, we propose an operator called quickDrop for semantic load shedding to gracefully decrease irrelevant data load. To validate the proposed approach, we developed a prototype and conducted a set of experiments. The obtained results show that the use of our approach significantly improves the performance of the system.

A fuzzy query engine for suggesting the products based on conformance and asymmetric conjunction

Most of the existing query engines built into modern e-commerce platforms retrieve products by means of text and numerical matching. Conventional query engines for conducting product search suffer from several problems because of which they are not good product recommendation engines. First, they solve tasks without considering semantic uncertainties in users’ questions and in data and therefore do not consider partial matching of query conditions. Users may need to input imprecise information to express their preferences or data describing products can be imprecise as well. Second, existing query engines used for conducting product search tasks do not consider conformance among tuples’ features and aggregation of atomic conditions of different nature. Fuzzy query approaches solve majority of problems related to semantic uncertainty, but do not cover all possible flexibilities to a sufficient level. When user defines sharp conditions (even though he might have values similar to provided ones in mind), entity either fully matches or fundamentally differs, regardless of the similarity between feature's value and the value appearing in conditions. This paper proposes a novel approach to suggesting suitable products based on fuzzy conformance between features appearing in the vector of the ideal product from customer's point of view and the features of existing products. In the second stage, several aggregations of atomic conditions for calculating overall matching between user's needs and available products in a data set are discussed. In addition, our approach is able to manage customers’ requirements expressed by constraints (features which should be met) and wishes (it is nice if these features are met) by the and if possible operator. Thus, asymmetric conjunction is an effective way to capture them. Experiments on a data set of used cars have shown promising results. Proposed fuzzy query engine considers some complex behaviors such as imprecise preferences, vague data in datasets, constraints and wishes in users’ requirements without previous history of queries, suggested answers, feedbacks, gathered ratings and customers’ personal data. It provides application in a specific domain of intelligent systems. The proposed framework can be adapted in a straightforward manner to any system for recommending suitable products, especially for the less-frequently purchased ones. Moreover, businesses that are not able to build or manage complex recommendation systems for gathering customer's behavioral data, as well as customers who are concerned with privacy risks might have benefit from this approach.

Natural Language Query Approach for Embodied Conversational Agent

Natural Language Query Approach for Embodied Conversational Agent

Improved Information Retrieval Performance on SQL Database Using Data Adapter

The NoSQL databases, short for Not Only SQL, are increasingly being used as the number of big data applications increases. Most systems still use relational databases (RDBs), but as the number of data increases each year, the system handles big data with NoSQL databases to analyze and access data more quickly. NoSQL emerged as a result of the exponential growth of the internet and the development of web applications. The query syntax in the NoSQL database differs from the SQL database, therefore requiring code changes in the application. Data adapter allow applications to not change their SQL query syntax. Data adapters provide methods that can synchronize SQL databases with NotSQL databases. In addition, the data adapter provides an interface which is application can access to run SQL queries. Hence, this research applied data adapter system to synchronize data between MySQL database and Apache HBase using direct access query approach, where system allows application to accept query while synchronization process in progress. From the test performed using data adapter, the results obtained that the data adapter can synchronize between SQL databases, MySQL, and NoSQL database, Apache HBase. This system spends the percentage of memory resources in the range of 40% to 60%, and the percentage of processor moving from 10% to 90%. In addition, from this system also obtained the performance of database NoSQL better than SQL database.