A Deep Dive into a Groundbreaking Approach to Machine Learning-Powered E-Learning

In cloud era as the data stored is enormous, the efficient retrieval of data with reduced latency plays a major role. This paper proposes the CBF: Cloud Bloom Filter - a novel mechanism for metadata management in the cloud computing database which improves the search efficiency of data. This paper proposes a three layered cloud metadata architecture which facilitates keyword based information retrieval that provides an efficient retrieval of exact data from cloud data server by reducing the search space. The attributes of metadata model is designed in such a way that the query is mapped to the exact location of the data in the data server, leads to the speedy retrieval of data from the data servers. CBF is used to reduce the disk lookups for non-existent files. Avoiding costly disk lookups considerably increases the performance of a database query operation. GBF, Global Bloom Filter provides the protocol for placing the metadata file in the metadata server and its replica location. LBF, Local Bloom Filter in the metadata server effectively reduces the time taken to update the respective file with limited updating overhead. CBF takes care of supply of the updated data instead of stale data to the user. The proposed scheme significantly reduces the time taken to retrieve the data from the data server.

The management and protection of catchment water resource are effective measures to promote the harmonious coexistence of human and nature, and accelerate the construction of ecological civilization. Effective storage, management, and retrieval of large spatial temporal data in catchment water resource are facing enormous challenges. At the same time, higher requirements are put forward for data concurrent access support capability and security reliability. Therefore, it is urgent to carry out research on intelligent management and control of large spatial temporal data in catchment water resource. This paper develops a hybrid architecture storage and retrieval system for large spatial temporal data of catchment water resource, which solves the problems of efficient storage and retrieval of massive multi-source heterogeneous data and concurrent access support. Combined with the technical specifications of water resources and geographic information related countries and industries, the existing water-related management system is migrated and integrated by using the “one-source-one-repository” model, avoiding repeated collection and storage of observation data, improving data consistency, and facilitating data sharing among various subsystems. HBase-based tile pyramid storage is used to implement fast visual display and query of data. Metadata model based on MongoDB document model is used to simplify metadata description. At the same time, the Elasticsearch search engine is used to build metadata full-text index, which provides multiple matching methods such as exact matching, fuzzy search, and range query. Spatial vector feature data storage model is established based on GeoJSON and MongoDB, build spatial index, design auxiliary index to accelerate data query and filtering, design sharing strategy in shared replication cluster, balance the contradiction between data distribution and query efficiency.

Database systems are pervasive components in the current big data era. However, efficiently managing and querying grid-based or array-based multidimensional climate data are still beyond the capabilities of most databases. The mismatch between the array data model and relational data model limited the performance to query multidimensional data in a traditional database when data volume hits a cap. Even a trivial data retrieval on large multidimensional datasets in a relational database is time-consuming and requires enormous storage space. Given the scientific interests and application demands on time-sensitive spatiotemporal data query and analysis, there is an urgent need for efficient data storage and fast data retrieval solutions on large multidimensional datasets. In this paper, we introduce a method for multidimensional data storing and accessing, which includes a new hash function algorithm that works on a unified data storage structure and couples with the memory-mapping technology. A prototype database library, LotDB developed as an implementation, is described in this paper, which shows promising results on data query performance compared with SciDB, MongoDB, and PostgreSQL.

This study tries to offer a complete review of database concepts, semantics, and query languages. A data model is a theoretical data representation that specifies the structure, connections, and restrictions of data in a certain area. It allows you to organise and categorise data for easier organisation, access, and management. A query language is a kind of computer programming language that is used to get information from a database. It enables users to describe the data they wish to obtain as well as the criteria under which the data must be provided. In the overall context of data modelling and query languages, semantics refers to the interpretation and significance of data and questions. It entails setting the rules and limitations for data storage, retrieval, and manipulation, as well as verifying that query results are consistent and relevant. Data modelling, query language, and semantics work together to create the basis of database management systems, allowing for efficient and effective data storage and retrieval for a broad variety of applications. We intend to give a greater knowledge of these ideas and their usefulness in data management by exploring the strengths and shortcomings of various data models and query languages, as well as highlighting the problems and applications of semantics. Here, also determines the expansion for the global usage of EHR systems and the demand for efficient data management services which built for the betterment of health records. A precise literature study was carried out, encompassing a wide variety of publications and themes such as Data Models, Semantics, Query Languages, the importance of these ideas in database systems, and an assessment of the advantages and drawbacks of numerous data models.

The purpose of this research is to provide practioners with information from the perspective of end users, database administrators, and developers regarding concerns and benefits of data management, data access, data manipulation, data query, and data storage in today’s workplace. Synergy between data managers and end users is also a topic of discussion in this paper. The research results find that end users requirements are growing, organizational data needs are expanding, and database administrators and developers have more data challenges due to the increase of data security requirements and administrative processes within organizations. Due to the increasing data needs, end users are becoming more adept in creating their own reports. The reporting techniques available to end users must ensure data security and integrity standards are in place. Developers of graphical user interface (GUI) tools are responsible for safeguarding data. Database administrators and developers must always think of data security throughout the entire lifecycle of the data when coding applications to ensure data security is consistent, even after it leaves the premise. Cloud computing and big data is also an important aspect of data management in the future. An examination of cloud computing and big data constructs on existing data is also important as organizations adopt new technologies.

Secure and efficient data retrieval over encrypted data using attribute-based encryption in cloud storage

With the fast development of industrial Internet of things (IIoT), a large amount of data is being generated continuously by different sources. Storing all the raw data in the IIoT devices locally is unwise considering that the end devices’ energy and storage spaces are strictly limited. In addition, the devices are unreliable and vulnerable to many threats because the networks may be deployed in remote and unattended areas. In this paper, we discuss the emerging challenges in the aspects of data processing, secure data storage, efficient data retrieval and dynamic data collection in IIoT. Then, we design a flexible and economical framework to solve the problems above by integrating the fog computing and cloud computing. Based on the time latency requirements, the collected data are processed and stored by the edge server or the cloud server. Specifically, all the raw data are first preprocessed by the edge server and then the time-sensitive data (e.g., control information) are used and stored locally. The non-time-sensitive data (e.g., monitored data) are transmitted to the cloud server to support data retrieval and mining in the future. A series of experiments and simulation are conducted to evaluate the performance of our scheme. The results illustrate that the proposed framework can greatly improve the efficiency and security of data storage and retrieval in IIoT.

With the substantial growth in vehicle population, the road safety problems, such as congestions and accidents, have become serious. To improve driving safety, efficient and rapid road safety data retrieval becomes a significant measure. As a new type of network, the named data networking (NDN) can effectively improve the data retrieval efficiency. Therefore, NDN might help improve the road safety data retrieval efficiency if it is introduced to the vehicular network, namely vehicular NDN. However, in the vehicular network, each vehicle works as a content router and high mobility of vehicles leads to frequent changes in network topology, which makes it hard to maintain forwarding information and achieve aggregation. Moreover, a large number of vehicles are involved in reverse paths, which leads to frequent reverse path disruptions and even data retrieval failures. To reduce the data retrieval latency and cost and improve the success rate, we propose an efficient road safety data retrieval approach that achieves push-based and pull-based data retrieval. In the approach, a forwarding network is constructed to improve the network stability. Based on the forwarding network, only a small number of vehicles are involved in pushing data, and consumers employ aggregation to pull data without depending on forwarding information. Moreover, the disrupted reverse paths can be effectively repaired to guarantee the successful receipt of data. Finally, this method is evaluated, and the experimental results show that it achieves the aforementioned objectives.

This paper investigates the development of an intelligent data query framework through the use of semantic web technologies for manufacturing purposes. The primary objectives of the ontology-based data query were to develop an efficient and scalable data interoperability and retrieval system; in order to find the most relevant query results with minimum message cost, most hits per query and least response time. This document explains the idea of ontology and the application of the same in the manufacturing domain. A computer simulation software was developed based on a real case study of distributed networks of manufacturing workshops. In this research, a semantic query algorithm was developed where query results are returned by investigating the semantic richness of each workshop. Results were compared with a semantic-free search mechanism based on key performance indicators. The results show the validity of the proposed model for efficient data query when compared to random search.

Two required features of a data monetization platform are query and retrieval of the metadata of the resources to be monetized. Centralized platforms rely on the maturity of traditional NoSQL database systems to support these features. These databases for example MongoDB allows for very efficient query and retrieval of data it stores. However, centralized platforms come with a bag of security and privacy concerns, making them not the ideal approach for a data monetization platform. On the other hand, most existing decentralized platforms are only partially decentralized. In this research, we developed Cowry, a platform for publishing of metadata describing available resources (data or services), discovery of published resources including fast search and filtering. Our main contribution is a fully decentralized architecture that combines blockchain and traditional distributed database to gain additional features such as efficient query and retrieval of metadata stored on the blockchain.

In cloud era as the data stored is enormous, efficient retrieval of data with reduced latency plays a major role. In cloud, owing to the size of the stored data and lack of locality information among the stored files, metadata is a suitable method of keeping track of the storage. This paper describes a novel framework for efficient retrieval of data from the cloud data servers using metadata with less amount of time. Performance of queries due to availability of files for query processing can be greatly improved by the efficient use of metadata and its analysis thereof. Hence this paper proposes a generic approach of using metadata in cloud, named ‘MaaS—Metadata as a Service.’ The proposed approach has exploited various methodologies in reducing the latency during data retrieval. This paper investigates the issues on creation of metadata, metadata management and analysis of metadata in a cloud environment for fast retrieval of data. Cloud bloom filter, a probabilistic data structure used for efficient retrieval of metadata is stored across various metadata servers dispersed geographically. We have implemented the model in a cloud environment, and the experimental results show that methodology used is efficient in increasing the throughput and also by handling large number of queries efficiently with reduced latency. The efficacy of the approach is tested through experimental studies using KDD Cup 2003 dataset. In the experimental results, proposed ‘MaaS’ has outperformed other existing methods.

One challenge in Earth science research is the accurate and efficient ad-hoc query and retrieval of Earth science satellite sensor data based on user-defined criteria to study and analyze atmospheric events such as tropical cyclones. The problem can be formulated as a spatio-temporal join query to identify the spatio-temporal location where moving sensor objects and dynamic atmospheric event objects intersect, either precisely or within a user-defined proximity. In this paper, we describe an efficient query and retrieval framework to handle the problem of identifying the spatio-temporal intersecting positions for satellite sensor data retrieval. We demonstrate the effectiveness of our proposed framework using sensor measurements from QuikSCAT (wind field measurement) and TRMM(precipitation vertical profile measurements) satellites, and the trajectories of the tropical cyclones occurring in the North Atlantic Ocean in 2009.

Wireless Data Broadcast is an efficient data dissemination method for public information to a large number of mobile/wireless clients. With the advance of the fourth-generation wireless communication system (4G), mobile devices may embed multiple-input multiple-output (MIMO) antennae to setup multi-connections to a base station. In this paper, we deal with data retrieval problem for mobile clients with MIMO antennae to retrieve a set of indexed data from parallel communication channels. Our purpose is to construct fast and energy efficient data retrieval protocols to reduce the response time and energy consumption. We name this problem as parallel data retrieval scheduling with MIMO Antennae (PADRS-MIMO), and propose two greedy heuristics named Least Switch Data Retrieval Protocol (Least-Switch) and Best First Data Retrieval Protocol (Best-First). We are the first work to deal with data retrieval with MIMO antennae for wireless data broadcast. We analyze the performances of Least-Switch and Best-First both theoretically and practically. Simulation results prove the efficiency of the two protocols.KeywordsData ItemData RetrievalAccess LatencyBroadcast ChannelMobile ClientThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Modern digital consumer devices are now enabled to handle certain types of XML data. In many cases, such data are periodically downloaded from remote via some network. In this paper, methods of storing and querying of XML data within digital devices are considered where relatively large scale XML data are downloaded in packetized formats for secure and efficient delivery from the senders to the receiving devices. Specifically, the downloaded XML fragments are natively organized by the fragment-oriented data structures and the supporting indexes both of which can be easily implemented on top of a lightweight relational storage. The proposed design provides efficient search and retrieval of XML data as well as dynamic update and versioning within resource-limited digital devices.

The technique of distributed storage coding has been widely used in wireless sensor networks for increasing the robustness of data storage and efficiency of data retrieval. Existing works mainly focus on scenarios in which each storage node stores a linear combination of a subset of K data packets generated by different source nodes. By solving the linear equations, a collector can recover all the K data packets with high probability. This paper explores the problem of flexible and efficient data dissemination and retrieval in wireless sensor networks. Our scheme exploits the broadcast nature of wireless transmission and improves the traditional random walk algorithm for efficient data dissemination. Furthermore, through the use of Fountain codes in data encoding, it enables a mobile collector to recover up-to-date data generated by any subset of source nodes. Specifically, by querying any t(1 + ε) storage nodes at the t-th time slot, the target data can be retrieved without having to decode all the source packets. Simulation results validate our analysis and show that the proposed schemes are flexible and efficient.