Transactional Queries Research Articles

Research on the Design of Digital Teaching Resource Management and Sharing Platform in Colleges and Universities Based on Cloud Storage

Abstract In this paper, after analyzing the structural system of the digital resource sharing model in colleges and universities, a shared storage model based on cloud storage is designed, and a digital teaching resource management and sharing platform for colleges and universities based on cloud storage is constructed on this basis. In order to realize accurate sharing, the sharing behaviors of teaching resources are symbolically illustrated, double behavior aggregation is performed, and a multidimensional preference personalized recommendation algorithm, i.e., DB-CGAT model, is designed to construct the personalized sharing module of teaching resources that integrates the behavioral graph. In addition, three types of experiments are conducted on the personalized sharing module of the platform to test its sharing performance and overall performance. The results show that the accuracy of this model is reduced by 0.04 compared with the traditional algorithm; the 90 percent time of login transaction is 2.44, and the time of query transaction is 1.46, which is all up to the standard value. All the action actions are green, and the number of clicks and throughputs in 5~30ss are around 120. Universities can benefit from the digital teaching resource management and sharing platform designed in this paper, which can improve teaching efficiency.

Cogni-Sec: A secure cognitive enabled distributed reinforcement learning model for medical cyber–physical system

The advent of the Internet of Things (IoT) has resulted in significant technical development in the healthcare sector, enabling the establishment of Medical Cyber–Physical Systems (MCPS). The increased number of MCPS generates a massive amount of privacy-sensitive data, hence it is important to enhance the security of devices and data transmission in MCPS. Earlier several research studies were undertaken in order to enhance security in healthcare, but none of them could adapt to changing behaviors of data attacks. Here the role of blockchain and Reinforcement Learning (RL) comes into play since it can adjust itself to the nature of changing attacks, thus preventing any kind of attacks. This work proposes a solution, named Cogni-Sec, which employs a decentralized cognitive blockchain and Reinforcement Learning architecture and addresses the security issue. Blockchain is incorporated in the approach for data storage and transmission to increase the degree of security in the MCPS modules. Hyperledger Fabric is applied as the blockchain base which shows transaction query results with nearly 10% increased throughput, 69% less memory consumption, and 15% lower CPU usage when compared to Ethereum. Further security risk at the block mining level within a blockchain network is reduced by introducing distributed Reinforcement Learning architecture in replacement for the miner nodes, which imitates the cognitive behavior of miners in a distributed environment. Different multi-agent learning systems have been evaluated for building the mining agent. Among these, the a3c agent in distributed learning setup yields the optimum cumulative reward with a median value of 54.5 and minimizes the maximum number of data threats.

Addressing blockchain privacy and efficiency challenges in mobile environments: an optimization strategy for lightweight clients and full nodes

In this paper, we propose an optimization strategy for privacy and efficiency challenges of blockchains in mobile environments. We propose a system model and define the corresponding threat model for the transaction query privacy problem of lightweight clients. Based on this, we design a transaction query scheme that effectively protects the privacy of lightweight clients. This scheme successfully achieves the security, privacy and completeness of transaction queries through an optimized blockchain restructuring process and privacy protection measures. Simulation experimental results show that our scheme significantly improves the privacy protection of transaction queries for lightweight clients while maintaining efficient query performance. In addition, we propose an optimization scheme for the lightweight full-node block verification efficiency problem. This scheme optimizes the verification process and introduces parallel computation, thus significantly improving the block verification efficiency. The experimental results show that our scheme achieves about 50% improvement in verification efficiency, both in terms of network size and transaction correlation. The research results in this paper not only provide a new perspective on privacy protection and efficiency improvement of blockchain technology, but also open up new applications in the mobile environment possibilities.

DLBN: Group Storage Mechanism Based on Double-Layer Blockchain Network

Blockchain, which stores data in an appending form, cannot achieve the purpose of expanding the storage capacity by increasing the number of nodes. As the system runs, nodes will face problems of insufficient storage space. In the existing peer-to-peer (P2P) blockchain network model, all network nodes participate in data storage, and the generated blocks need to be verified among the network-wide nodes. This approach suffers from low system transaction throughput and data storage redundancy. In order to solve the above existing problems, this article proposes a block data storage model based on the double-layer blockchain network (DLBN), which improves the internal data composition structure of the blockchain. The DLBN contains two types of blockchain nodes, which form the storage and consensus layers of the system, respectively. The consensus layer is responsible for tasks, such as transaction sequencing, validation, and block packing, thus increasing the system transaction throughput. The nodes in the storage layer are divided into multiple storage units (SUs), and all nodes in the SU jointly maintain a copy of the complete blockchain, thereby reducing the storage pressure on the nodes. Based on the DLBN model, we design a reputation-based consensus mechanism, block storage allocation algorithm, and transaction query optimization algorithm, respectively. Through experimental verification and analysis, the storage model based on the DLBN can effectively improve the system transaction throughput and reduce the node storage capacity while ensuring system security.

Grid based blockchain framework for information management in surgical process proceedings

Numerous academic and industrial research studies have focused on the feasibility of implementing blockchain-based healthcare systems data management that can transform the way data systems functioning, usage, and development can be effective. In this manuscript, the focus is on the development of a Grid-based blockchain structure formanaging the surgical process-related information in integrated systems. Considering the implications, scope of human error in the surgical processes, and the quantum of records integral to such process, the need for having robust records of surgical data is imperative. Aiming at improving the security of the information, in this manuscript, the Grid-block structure constituting multiple grids framework is proposed, which can be optimal for transaction query processing, responsiveness for triggers, and security inheritance. The proposed system, if chosen for real-time implementation, can support interoperability of data among the distinct healthcare systems and support in minimizing the issues of human error inspections, prevention scope for errors whilst improving the quality of healthcare solutions.

Analysis of Agricultural Product Information Traceability and Customer Preference Based on Blockchain

For traditional agricultural product traceability system, there are some problems, such as information asymmetry and serious centralized storage. To solve the problems, a set of an agricultural product traceability information analysis system based on blockchain is designed. The overall framework of the agricultural product information traceability system is built, and the traceability model and process are designed in detail from the data layer, consensus layer, and contract layer. Collaborative verification function modules are added in the data layer to ensure the authenticity of data on the blockchain. The integral penalty mechanism is introduced to improve the practical Byzantine fault tolerance algorithm (PBFT), which ensures the security of the block network and the validity of data. Smart contracts are adopted to ensure that changes in agricultural information are actually recorded on blockchain. The results show that the designed blockchain-based agricultural product information traceability system solves the problem of difficult sharing and traceability of agricultural product information, which realizes the traceability of agricultural product production information, processing information, transportation information, and transaction query information, and has certain theoretical and practical value for the information traceability of agricultural products and other products.

Query Dictionary for Frequent Non-Indexed Queries in HTAP Databases

The increasing demand for the simultaneous transaction and review of the data for either decision making or forecasting has created a need for faster and better Hybrid Transactional/Analytical Processing (HTAP). This paper emphasizes the speedup of Online Analytical Processing (OLAP) operations in an HTAP environment where analytical queries are mainly repetitive and contain non-indexed keys as their predicates. Zone maps and materialized views are popular approaches adopted by more extensive databases to address this issue. However, they are absent in in-memory databases because of space constraints. Instead, in-memory databases load the cache with result pages of frequently accessed queries. Increasing the number of such queries can fill the cache and raise the system’s overhead. This paper presents Query_Dictionary, a hybrid storage solution that leverages the full capabilities of SQLite by retaining less information of repetitive queries in the cache and efficiently accommodating the newly updated data by the end-user. The solution proposes storing page-level metadata query information for a larger result set and row-level information for a smaller result set. It demonstrates Query_Dictionary capabilities on three types of representative queries: single table, binary join, and transactional queries on non-indexed attributes. In comparison with SQLite, the proposed method performs better.

Usability effectiveness of a federated search system for electronic theses and dissertations in Nigerian institutional repositories

PurposeThe purpose of this study was to evaluate the usability effectiveness of a webware for electronic theses and dissertations (ETDs) in Nigerian repositories. The webware (etdsearch.com.ng) is a web application system that curates ETDs from three sampled Federal government-owned universities. The system also links users to the repositories where the theses and dissertations are hosted.Design/methodology/approachThe case study research strategy was adopted for the study. Sixty postgraduate students from three universities were randomly selected. A usability evaluation questionnaire based on the ISO 9241-11 framework was used to collect data after performing pre-defined queries/tasks based on the informational and transactional query models. The research questions were analysed using the median of the performance score (fx) of the three universities for each item evaluated, while the Kruskall–Wallis test by ranks was used to test the null hypothesis at a 5% level of significance.FindingsThe study answered two research questions and tested two null hypotheses on the usability effectiveness of the webware based on the informational and transactional queries. The participants found the ETD search system effectively useable. In addition, there was no significant difference in the opinions of the participants.Research limitations/implicationsThe webware used simulated repositories as a feed bed for the ETDs in order to have control over the workability of the repositories. Thus, the results may differ slightly when “live” repositories are used.Practical implicationsThe effectiveness of a webware that aggregates ETDs in Nigerian repositories will present libraries in Nigeria with evidence on how these systems work and can be improved upon.Originality/valueThere is a dearth of literature on practical usability studies of digital information systems in Nigerian libraries.

Data Sharing Model of Internet of Things Based on Blockchain

Blockchain technology and Internet of Things technology are two new technologies formed in the current transmission of information technology. In the implementation of its technical control, it can scientifically control the information sharing work and realize the artificial adjustment of the transmission control of the Internet of Things technology. In this paper, research on the Internet of Things data sharing model based on block chain with a view to provide guidance to the security of Things data sharing technology under the block chain. In this paper, Hyperledger Fabric block chain platform-based platform, proposed a block-based chain of IOT data sharing model, security and data privacy is an enhancement, obtained by the performance of the test model. Throughput is maximized when the write transaction sending frequency is 100 TPS and the query transaction sending frequency is 250 TPS. The maximum write throughput is 60 TPS, which is better than Bitcoin and Ethereum on the public chain, which proves the feasibility of the model implementation. This model can achieve storage and sharing without the help of a third-party centralized organization, and directly establish trust between participants, which can ensure the safe sharing of data.

Practical quantum private query based on Bell state

Quantum private query (QPQ) is a cryptographic application that protects the privacy of both users and databases while querying the database secretly. In most existing QPQ protocols, the protection of user privacy can only be cheat-sensitive. Cheat-sensitive means that Bob will be found later with a certain probability if he tries to get the address queried by Alice. On the premise of cheat-sensitivity, although Alice can discover Bob’s malicious behavior after a query (transaction), the secret information of Alice was leaked in the completed query, which is likely to be a fatal blow to Alice. Or, to prevent Bob’s malicious behavior, Alice executes one or more additional queries to test Bob’s honesty. However, to bypass Alice’s honesty test, Bob can also provide several honest queries before performing dishonest queries. Therefore, cheat-sensitive should not be the ultimate goal of user privacy protection in QPQ. In this paper, we propose a practical QKD-based QPQ protocol with better user privacy protection than cheat-sensitivity based on order rearrangement of qubits. The proposed QPQ protocol can resist the Trojan horse attack even without wavelength filter and photon number splitter (PNS) equipped with auxiliary monitoring detectors.

Test-framework: performance profiling and testing web search engine on non factoid queries

<p>Performance profiling and testing is one of the interesting topics in the big data management and Cloud Computing. In testing, we use test cases composed to different type of queries to evaluate the performance aspects of the information retrieval system for large scale information collection. This test scenarioperforms the evaluation ofretrieval accuracy for all kind of ambiguity and non factoid queries with result set as Training data. This stands difficult to evaluate the retrieval method in order to schedule or optimize the Recommendation and prediction technique of the IR method to the Real time queries. The Queries is considered as requirement specification which has to supply to search engine or web information provider applications for information or web page retrieval. In this paper, we propose a novel technique named as “Test Retrieval Framework“a performance profiling and testing of the web search engines on the information retrieved towards non factoid queries. In this technique, we apply expectation maximization algorithm as an iterative method to find maximum likelihood estimate.We discuss on the important aspects in this work based on Recommendation models integrating domain and web usage, Query optimization for navigational and Transactional queries, Query Result records.The Experimental results demonstrates the proposed technique outperforms of state of arts approaches in terms of set based measures like Precision, Recall and F measure and rank based measures like Mean Average Precision and Cumulative Gain.</p>

Evaluating search effectiveness of some selected search engines

With advancement in technology, many individuals are getting familiar with the internet a lot of users seek for information on the World Wide Web (WWW) using variety of search engines. This research work evaluates the retrieval effectiveness of Google, Yahoo, Bing, AOL and Baidu. Precision, relative recall and response time were considered for this evaluation, a total of 24 search queries were sampled based on information queries, navigational queries and transactional queries. They are categorized into single word queries, double word queries, sentence queries and alphanumeric queries. Finally the overall average of all the designated queries shows Bing has the best score based on relative recall with 0.54, Google has the best response time with 0.39s and has the highest precision with 1.37.

Web Service for Incremental and Automatic Data Warehouses Fragmentation

The data warehouses (DW) are proposed to collect and store heterogeneous and bulky data. They represent a collection of thematic, integrated, non-volatile and histories data. They are fed from different data sources through transactional queries and offer analytical data through decisional queries. Generally, the decisional queries execution cost on large tables is very high. Reducing this cost becomes essential to enable decision-makers to interact in a reasonable time. In this context, DW administrators use different optimization techniques such as fragmentation, indexing, materialized views, and parallelism. On the other hand, the volume of data residing in the DW is constantly evolving. This can increase the complexity of frequent queries, which can degrade the performance of DW. The administrator always has to manually design a new fragmentation scheme from the new load of frequent queries. Having an automatic fragmentation tool of DW becomes important. The approach proposed in this paper aims at an incremental horizontal fragmentation technique of the DW through a web service. This technique is based on the updating of the queries load by adding the new frequent queries and eliminating the queries which do not remain frequent. The goal is to automate the implementation of the incremental fragmentation in order to optimize the new queries load. An experimental study on a real DW is carried out and comparative tests show the satisfaction of our approach.

Performance Optimization of Row-Level-Data-Security for Transactional Business Intelligence Queries in Cloud and SaaS Business Applications

Objectives: To optimize row-level data security for Transactional Business Intelligence (TBI) SQL queries to reduce complexity and enable the back-end database to create better optimized execution plans that perform and scale well. Methods/Statistical Analysis: Benchmark experiments were conducted using Oracle RDBMS 11gR2 using representative SQL queries from Oracle’s Fusion CRM TBI Applications for five different users with multiple, varying roles and data access permissions. All four measures of SQL performance viz. SQL response time (RT), Input-Output (IO) Buffer Gets, Hard-Parse-Time and Shared Memory utilization were recorded with and without our proposed optimizations. The four performance measures were then compared to record actual improvements. Findings: The benchmark experiments established very promising results. We recorded repeatable, significant gains in not only the four measures of individual SQL performance but also at the database resources level. The proposed architecture enables the creation of a hand-shake mechanism between the application and security frameworks to optimize query and application performance. This is done by creating name-value pairs of roles and filter criteria and passing these from the middleware/application to the security framework at run time. The result is a pruned version of the final physical SQL, retaining only relevant RDSPs while removing ones not logically needed. Such optimized row-level data security makes TBI SQL queries less complex and the back-end database is able to create better optimized execution plans that perform and scale well. Query Response Time (RT) improvements ranging from 5% to 745 times, Hard-Parse Time improvements from 1% to 208 times, Logical I/O or Buffer Gets’ improvement ranging from 43% to 454 times and SQL-Shared-Memory reduction by up to 52%. Application/Improvements: Our proposed architecture is directly applicable to improve performance of all TBI applications that use row-level data security, especially in the Software-as-a-Service (SaaS) and Cloud Models.

Search engine effectiveness using query classification: a study

Purpose – The purpose of this paper is to highlight the retrieval effectiveness of search engines taking into consideration both precision and relative recall. Design/methodology/approach – The study is based on search engines that are selected on the basis of Alexa (Actionable Analytics for the web) Rank. Alexa listed top 500 sites, namely, search engines, portals, directories, social networking sites, networking tools, etc. But the scope of study is confined to only general search engines on the basis of language which was confined to English. Therefore only two general search engines are selected for the study . Alexa reports Google.com as the most visited website worldwide and Yahoo.com as the fourth most visited website globally. A total of 15 queries were selected randomly from PG students of Department of Library and Information Science during a period of eight days (from May 8 to May 15, 2014) which are classified manually into navigational, informational and transactional queries. However, queries are largely distributed on the two selected search engines to check their retrieval effectiveness as a training data set in order to define some characteristics of each type. Each query was submitted to the selected search engines which retrieved a large number of results but only the first 30 results were evaluated to limit the study in view of the fact that most of the users usually look up under the first hits of a query. Findings – The study estimated the precision and relative recall of Google and Yahoo. Queries using concepts in the field of Library and Information Science were tested and were divided into navigational queries, informational queries and transactional queries. Results of the study showed that the mean precision of Google was high with (1.10) followed by Yahoo with (0.88). While as, mean relative recall of Google was high with (0.68) followed by Yahoo with (0.31), respectively. Research limitations/implications – The study highlights the retrieval effectiveness of only two search engines. Originality/value – The research work is authentic and does not contain any plagiarized work.

Determining the Impact of Domain Independent Ontology over Transactional User Queries

Searching has become part and parcel of our human life. This has been possible because of the significant improvement that has happened in the field of information retrieval. Any Information Retrieval application can be considered as a collection of different components namely Query Interface where the user enters their information need, the document representation which is done using the indexing process, the matching process which compares the query and the document representation that is available in the index, and finally the ranking process which arranges the documents according to their user query relevance. Lot of work has been done by commercial search engine organizations to make searching a rewarding experience to the users. One such area is to know the user intention behind the queries that is being given in the query interface. Based on the user intention, user queries are being classified as informational, navigational and transactional queries. To make queries more responsive for Information Retrieval, Query Refinement can be done on the individual queries. In recent times ontology has been used in query refinement process and many studies in this area has shown considerable improvement with respect to the results retrieved. In this paper we would like to study the impact of Domain independent ontology in the query refinement process involving transactional queries.

QUERY TUNING IN ORACLE DATABASE

Oracle database is well suited to use for managing and exchanging of corporate data, especially for companies that have large-scale databases. Even though Oracle can work best, however, there are conditions where Oracle does not perform well and consume more time to perform the query transaction. This is when the database tuning process is needed. This process will solve the problems of low performance and high execution time when accessing data in the database. This study aims to propose an improvement process of query tuning that can reduce queries execution time. The cause of the problems can be discovered by reviewing the source code, List of Value (LOV) and view on the current system. To enhance the query, existing query scripts need to be modified to fit the tuning process. As an evaluation on the effectiveness of the proposed query tuning process, a comparison of study is done. The result of the study shows that before the tuning process, a system takes 156 milliseconds to execute the query and after implementing the proposed query tuning process, the execution time decreases to less than one millisecond. It means hundred times performance improvement on the query execution. The outcome of the study proved that the query execution time decreased after implementing the proposed query tuning process in the system.

CONTINUOUS MULTIPLE OLAP QUERIES FOR DATA STREAMS

Querying data stream data continuously has been addressed mostly as transactional queries with some attempts at analytical processing. But, in most of the proposals a single query is executed for a given window of data. In this paper, we propose to continuously execute multiple related OLAP queries (CMOLAP) for the data chosen from a data stream. The chosen data defines the context. The context data is temporarily stored in the form of a multidimensional cube to perform OLAP operations. Three sets of operations are defined. The first converts the data in a stream to a context, the second allows altering the context and the third set is analytical which operates on the context and produces an output stream. More than one related analytic operation can be performed for the data in a context. The sequence of operations, referred to as context queries, is continuously executed for a time-based window. As a result it is possible to do enhanced related analysis of data. We have also developed a GUI interface where the queries can be expressed in a user friendly manner.

Query classification and study of university students' search trends

PurposeThis study is an attempt to develop an automatic identification method for Arabic web queries and divide them into several query types using data mining. In addition, it seeks to evaluate the impact of the academic environment on using the internet.Design/methodology/approachThe web log files were collected from one of the higher institute's servers over a one‐month period. A special program was designed and implemented to extract web search queries from these files and also to automatically classify Arabic queries into three query types (i.e. Navigational, Transactional, and Informational queries) based on predefined specifications for each type.FindingsThe results indicate that students are slowly and gradually using the internet for more relevant academic purposes. Tests showed that it is possible to automatically classify Arabic queries based on query terms, with 80.6 per cent to 80.2 per cent accuracy for the two phases of the test respectively. In their future strategies, Jordanian universities should apply methods to encourage university students to use the internet for academic purposes. Web search engines in general and Arabic search engines in particular may benefit from the proposed classification method in order to improve the effectiveness and relevancy of their results in accordance with users' needs.Originality/valueStudying internet web logs has been the subject of many papers. However, the particular domain, and the specific focuses on this research are what can distinguish it from the others.

Retrieval Effectiveness Analysis for Anchor Texts

锚文本对网络信息检索性能的提升作用已经得到验证,并被广泛地应用于商用网络搜索引擎.然而,锚文本制作的不可控性导致其中蕴含大量与目标网页不相关或具有作弊倾向的无用信息.另外,对于需要衡量检索结果服务质量的事务类查询,原始锚文本推荐的目标网页也往往与真实的用户体验不一致.为了解决上述问题,基于大规模真实用户的互联网浏览行为日志展开研究.首先提出了锚文本检索有效性的评估框架,然后分析了用户网络浏览点击行为与锚文本检索有效性之间的联系,挖掘了用户网络浏览点击行为中有助于筛选高质量锚文本的特征.基于这些特征,提出了两种超链接文档生成方法.实验结果表明,基于用户网络浏览点击行为特征筛选出的锚文本,与原始锚文本相比,能够明显地提升网络检索的性能.;Anchor texts have been extensively used and have proven to be effective over the years in commercial Web search engines; however, anchor texts are created freely without any supervision, which causes there to be a large number of noises and spam in anchor texts. Moreover, for transactional queries, which need measurements of service quality, destination, Web pages of anchor texts are not always consistent with user experience. To overcome these problems, this paper focuses on the large scale of the user Web browsing behavior data. This paper first proposes a framework for a retrieval effectiveness measurement. Then, based on this framework, analyze the relation between the user Web browsing behavior data and retrieval effectiveness of anchor texts is analyzed. The properties of a user Web browsing behavior, which are useful for Web search, are mined and quantified. Experimental results show that the proposed method offers an accurate evaluation on the retrieval effectiveness of anchor texts.