Query Retrieval Time Research Articles

Transforming data-centric eXtensible markup language into relational databases using hybrid approach

eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.

Transforming data-centric eXtensible markup language into relational databases using hybrid approach

eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.

Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP)

Background : As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.

Improving the support for XML dynamic updates using a hybridization labeling scheme (ORD-GAP).

Background: As the standard for the exchange of data over the World Wide Web, it is important to ensure that the eXtensible Markup Language (XML) database is capable of supporting not only efficient query processing but also capable of enduring frequent data update operations over the dynamic changes of Web content. Most of the existing XML annotation is based on a labeling scheme to identify each hierarchical position of the XML nodes. This computation is costly as any updates will cause the whole XML tree to be re-labelled. This impact can be observed on large datasets. Therefore, a robust labeling scheme that avoids re-labeling is crucial. Method: Here, we present ORD-GAP (named after Order Gap), a robust and persistent XML labeling scheme that supports dynamic updates. ORD-GAP assigns unique identifiers with gaps in-between XML nodes, which could easily identify the level, Parent-Child (P-C), Ancestor-Descendant (A-D) and sibling relationship. ORD-GAP adopts the OrdPath labeling scheme for any future insertion. Results: We demonstrate that ORD-GAP is robust enough for dynamic updates, and have implemented it in three use cases: (i) left-most, (ii) in-between and (iii) right-most insertion. Experimental evaluations on DBLP dataset demonstrated that ORD-GAP outperformed existing approaches such as ORDPath and ME Labeling concerning database storage size, data loading time and query retrieval. On average, ORD-GAP has the best storing and query retrieval time. Conclusion: The main contributions of this paper are: (i) A robust labeling scheme named ORD-GAP that assigns certain gap between each node to support future insertion, and (ii) An efficient mapping scheme, which built upon ORD-GAP labeling scheme to transform XML into RDB effectively.

Query batching optimization in database systems

Techniques based on sharing data and computation among queries have been an active research topic in database systems. While work in this area developed algorithms and systems that are shown to be effective, there is a lack of rigorous modeling and theoretical study for query processing and optimization. Query batching in database systems has strong resemblance to order batching in the warehousing context. While the latter is a well-studied problem, the literature on optimization techniques for query batching problem is quite scarce/nonexistent. In this study, we develop a Mixed Binary Quadratic Program (MBQP) to optimize query-batching in a database system. This model uses the coefficients of a linear regression on the query retrieval time, trained by a large set of randomly generated query sets. We also propose two heuristics, the so-called restricted-cardinality search methods I and II, for solving the proposed MBQP. To demonstrate the effectiveness of our proposed techniques, we conduct a comprehensive computational study over randomly generated instances of three well-known database benchmarks. The computational results show that when the proposed MBQP is solved using the designed heuristics, an improvement of up to 61.8% in retrieving time is achieved.

XML-REG: Transforming XML Into Relational Using Hybrid-Based Mapping Approach

eXtensible Markup Language (XML) is one of the most used standards for information sharing between applications and devices, both on the internet and local network. However, relational database (RDB) has been used by many enterprises as their data management system and will require an amount of cost to change the system completely, if they are to change to XML technology solely. Thus, a mapping scheme is required to provide seamless integration on bridging XML technologies and RDBs. In this paper, an efficient model-based mapping scheme named XML-REG is proposed. The XML document will first be read and parsed into the parser, namely Streaming API for XML (StAX) parser. Then, each node will then be assigned with unique identification label to show the exact position of nodes in the document. Subsequently, by employing the proposed algorithm, data will then be transformed into tables in the RDB storage. As the result, two tables, namely (i) value table to store information carried by text node of the document, and (ii) path table to store the hierarchy structure of the document will be created. Experimental evaluations demonstrated that XML-REG outperformed some existing approaches, such as Mini-XML, XAncestor, XMap and XRecursive in terms of data storage size, mapping time and query retrieval time. In addition, the scalability test has also been conducted to show the capability of these approaches in supporting huge datasets, by scaling the DBLP dataset by times 5, times 10 and times 15. The results showed that XML-REG has the closest to linear graph compared to other existing approaches. On average, XML-REG showed the best performance in terms of query retrieval time and database storage size.

Trustworthy media news content retrieval from web using truth content discovery algorithm

Nowadays, the internet plays a major role in online information retrieval. As, we are well aware the web provides information related all the fields like national, lifestyle, movies, shopping, spirituality, sports, entertainment and much more. One cannot assume that the web retrieved information is believable or trustworthy due to multiple answers to the same query. The paper aims to study many research articles which are related to truth information discovery. However, the paper found that there is no proper research to find trustworthiness of news content which is extracted from multiple information sources with minimum misclassification error and retrieval time. To alleviate these issues, the truth content discovery algorithm is proposed to produce trustworthy information with minimal time along with multiple domain news information. The system provides reliable information from a various source of news provider with minimum classification error. The proposed method ranked the news content index based on query matches from extracted information. It minimizes the query retrieval time and classification error. Based on the experimental evaluation, proposed TCD + J48 algorithm produced the best result compared to existing approaches. It minimizes reduces 7.54 ms the query retrieval time (QRT), 5% Error rate (ER) and 0.98 and Mean Normalized Absolute Distance (MNAD).

An Effective and Trustable Spatial Service Recommendation Algorithm for Spatial Query Retrieval In Geo-Social Network

<span>The spatial information (e.g., restaurants/hotels) is related with the keyword(s) to indicate their businesses, services and features. The main issue of relevant information retrieval is to query an entity which includes a set of spatial query keywords and have the smallest amount of inter-object distance. The spatial queries with keywords have not been extensively explored. Still, the traditional method was focused on the multidimensional data. Previous works mostly targeted to predict the top-k Nearest Neighbors keyword query, where every keyword should be equivalent to the whole querying keywords. However, the mechanism does not consider the density of data entities in the spatial space. To overcome the above issues, An Effective and Trustable Spatial Service Recommendation (ETSSR) algorithm focuses on the most relevant information retrieval with the enhanced accuracy and minimal retrieval time for spatial information services. The main goal of work is to provide best spatial information retrieval with an accurateness of location prediction and minimal information retrieval time. The system minimizes the classification issue and visualization problem for spatial information </span><span>in Geospatial Social network. The system improves the spatial information retrieval with an accuracy of location prediction and minimizes the information retrieval time compare than existing methods. Based on Experimental estimations, proposed ETSSR+KNN enhanced 0.48 P (Precision) and 0.49 R (Recall) and minimized 28 milliseconds query retrieval time.</span>

To Design Most Related & Recurrent Recommendation Algorithm for Relevant Information Retrieval with Content Rating Predations Accuracy for Similar Content E-learner

Nowadays, E-learning is progressing in effective manner that improves the quality of education system in smarter way. Here, there is no age and time constraint for user to learn from web. the most of the widely recognized methods are utilized specific classification for learning content predictions. However, there are some difficulties still available to indentify, which content are more accurate and relevant to my query. There are some existing e-learning mechanisms suggested based on their personalization of learning content. However, it makes the entire learning process as complex and time consumption. to overcome these above issues, Most Related & Recurrent Recommendation (MRRR) technique is proposed for query analysis and learning content recommendation to expose the e-learning in better way. Proposed framework is arranged the content in interactive way with more graphical representation for high visualization. Here, content recommendation can be easily predicted with high accuracy for similar content learner. The techniques offer secure access and provisioning mechanism to retrieve most relevant content with minimal time. the paper main goal is to visualize learning content based on their prediction of content ranking for similar content. once, query is processed by user, the method parses the query into attributes to retrieve the most relevant information in effective way. The technique establishes semantic relationship between the properties and meta-information to gather information about visitor of particular learning content. the rating prediction is computed for learning content based previous user satisfaction report. Based on experimental evaluation, proposed techniques improved the content visualizations success rate and minimized the query retrieval time with accuracy content rating predations.

Hybridation of Labeling Schemes for Efficient Dynamic Updates

<p>With XML as the leading standard for data representation over the Web, it is crucial to store and query XML data. However, relational databases are the dominant database technology in most organizations. Thus, replacing relational database with a pure XML database is not a wise choice. One most prominent solution is to map XML into relational database. This paper introduces a robust labeling scheme which is a hybrid labeling scheme combining the beauty features of extended range and ORDPATH schemes to supports dynamic updates. In addition, we also proposed a mapping scheme based on the hybrid labeling scheme. Our proposed approach is evaluated in terms of (i) loading time, (ii) storage size, (iii) query retrieval time, and (iv) dynamic updates time, as compared to ORDPATH and ME schemes. The experimental evaluation results shows that our proposed approach is scalable to support huge datasets and dynamic updates.</p>

A Secured Cognitive Agent based Multi-strategic Intelligent Search System

Search Engine (SE) is the most preferred information retrieval tool ubiquitously used. In spite of vast scale involvement of users in SE’s, their limited capabilities to understand the user/searcher context and emotions places high cognitive, perceptual and learning load on the user to maintain the search momentum. In this regard, the present work discusses a Cognitive Agent (CA) based approach to support the user in Web-based search process. The work suggests a framework called Secured Cognitive Agent based Multi-strategic Intelligent Search System (CAbMsISS) to assist the user in search process. It helps to reduce the contextual and emotional mismatch between the SE’s and user. After implementation of the proposed framework, performance analysis shows that CAbMsISS framework improves Query Retrieval Time (QRT) and effectiveness for retrieving relevant results as compared to Present Search Engine (PSE). Supplementary to this, it also provides search suggestions when user accesses a resource previously tagged with negative emotions. Overall, the goal of the system is to enhance the search experience for keeping the user motivated. The framework provides suggestions through the search log that tracks the queries searched, resources accessed and emotions experienced during the search. The implemented framework also considers user security.

基于大数据技术的北斗导航信息服务性能提升

With the rapid development of the global satellite navigation system, navigation information services have been widely applied in various industries. Facing with the rapid exponential growth in the amount of navigation information services data, traditional data management mode merged a series of questions such as reducing storage efficiency, long query retrieval time and lacking intelligent data analysis methods, which has been unable to fully meet the satellite navigation services needs of the application. According to the existing problems of COMPASS satellite navigation system data management mode, the performance enhancement technology of COMPASS navigation information services based on COMPASS navigation big data was studied and proposed in this paper. The experiment results show that the data storage efficiency can be increased by more than 2.5 times and the user query latency can be reduced by about 90% through using big data technology in this paper, which has preliminary data intelligence analysis capabilities.

A New Randomized Order Preserving Encryption Scheme

Order Preserving Encryption (OPE) schemes have been examined to a great extent in the cryptography literature because of their prospective application to database design. OPE is an appealing method for database encryption as it permits to execute sort and range queries in an efficient manner without decrypting the data. Databases such as CryptDB are beginning to employ encryption to guard sensitive data. No existing OPE schemes that were proposed in the literature achieved IND-OCPA security except mutable Order-Preserving Encoding (mOPE) scheme, the first OPE scheme that satisfies IND-OCPA with respect to OPE encodings. However, mOPE scheme uses DET (deterministic encryption) to encrypt the plaintext values which leads to leakage of distribution of plaintext domain. This paper proposes a scheme called as Randomized Order Preserving Encryption (ROPE), a novel OPE scheme that leaks nothing beyond the order. ROPE follows the mOPE scheme by contributing randomness to it, so as to accomplish INDOCPA security. The ROPE scheme implements insert, delete and query functions on an encrypted MySQL database. ROPE scheme permits various SQL queries to be employed instantly on encrypted data.The performance of ROPE scheme is compared with the existing DOPE scheme and observed that there is a query retrieval time overhead. Still, ROPE scheme renders more confidentiality and attains the IND-OCPA security for OPE when compared to the existing OPE schemes. General Terms Security, Algorithms

Robust iris indexing scheme using geometric hashing of SIFT keypoints

This paper proposes an efficient indexing scheme for searching large iris biometric database that achieves invariance to similarity transformations, illumination and occlusion. The proposed scheme considers local descriptors as well as relative spatial configuration for claiming identity. To overcome the effect of non-uniform illumination and partial occlusion due to eyelids, local features are extracted from noise independent annular iris image using scale invariant feature transform (SIFT). The detected keypoints are used to index iris database by applying geometric hashing scheme that is robust to similarity transformations as well as occlusion. During iris retrieval, geometric hashed location from query iris image is obtained to access the appropriate bin of hash table and for every entry found there, a vote is casted. The iris images that receive more than certain number of votes are considered as possible candidates. In order to find the potential matches, the keypoint descriptor of the list of possible candidates is matched with the query iris. Since only small portion of database is scanned to find a match it reduces the query retrieval time and improves accuracy. This approach is tested on UBIRIS, BATH, CASIA and IITK iris databases and shows a substantial improvement over exhaustive search technique in terms of time and accuracy.