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In the development of physical theories, thought experiments play a crucial role. Research on this topic began in 1976 and has continued to the present. This study aims to provide a more complete picture of the progress of thought experiments over the past two decades. To achieve this, this study employs bibliometric mapping methods. A total of 679 published papers were analyzed, including articles (504), conference papers (92), and book chapters (83). This data was retrieved from the Scopus database. The study's findings reveal that research and publications on thought experiments are highly valued and have received significant attention over the past eight years. According to the findings, 90% of the top 20 source titles contributing to thought experiments are from journals in the first and second quartiles (Q1 and Q2). This quartile ranking shows the quality and significant influence of a journal. The geographical distribution indicates that the United States contributes the most to thought experiments research, with 213 documents, 2592 citations, and 47 links. We also identified several prospective keywords that could be the focus of future research, including artificial intelligence, physics education, fiction, God, theology, productive imagination, technology, speculative design, and critical design. Therefore, this study provides a thorough picture of thought experiment research trends and future directions of potential topics that can be the focus of future researchers.

The purpose of this study is to examine the publications conducted in education related to virtual worlds with the bibliometric analysis method. This study employed the appropriate keywords determined via digging the Web of Science database. A total of 2,978 publications meeting the criteria of the current study were examined. VOSviewer software was used for the analysis. We utilized co-authorship analysis to see the descriptive characteristics of the examined publications. The most conducted type was proceedings and the number of publications fluctuated over time with a peak in 2012. England, USA, and Spain had the most publications among the countries, and Open University, Coventry University, and the Polytechnic University of Bucharest had the most publications among the organizations. The majority of the studies were published in the journal of Computers & Education, British Journal of Educational Technology, and Interactive Learning Environments. For revealing the most common keywords, we used the co-occurrence analysis. "e-learning", "second life" and "higher education" were the most occurred keywords in the publications. The citations analysis was used to see which authors and publications were mostly cited in the examined papers. The most cited authors were Dede, C., Lan, Y. and Ketelhut, D. J. According to the analysis of the cited references, Dalgarno and Lee, (2010), Merchant, Goetz, Cifuentes, Keeney-Kennicutt, and Davis, (2014) and Dunleavy et al. (2008) were the most cited studies. Technological developments have affected the interest in the use of VWs in education. As a new type or extension of virtual worlds, metaverse promises new opportunities in education. The findings of this study would present a road map for those future research to see the general picture of the relevant area.

A systematic review was carried out on the production and publication of research papers related to the study of the variable Literature of Oral Tradition, Social Justice and Inclusive Education under the PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) approach. The purpose of the analysis proposed in this document was to know the main characteristics of the publications registered in the Scopus and WoS databases during the year and their scope in the study of the proposed variables, achieving the identification of 106 publications. Thanks to this first identification, it was possible to refine the results through the keywords entered in the search button of both platforms, which were "social justice" and "inclusive education," reaching a total of 18 documents, excluding duplicates and those that did not meet the analysis criteria. These were analyzed to find out the relationship between the variables and how the Americas have advanced in the fight against corruption. Among the main findings, one of the factors taken into account through oral tradition literature to achieve inclusive education is the recognition of plurality and cultural differences evident in society. For them, the authors highlighted cultural expressions transmitted from generation to generation creating an environment ready for the generation of new knowledge by supporting the diversity identified in the community, which translates into social justice through an education that recognizes differences without exclusion.

Mobile learning enables organizations to meet the educational demands of digital native learners in the digital era. Education at the vocational high school level also makes use of it, as this level of education has unique characteristics in terms of the type of learning that occurs, notably the emphasis of practice-based learning. There has not been a study on the usage of mobile learning at the vocational high school level that has used the bibliometric method to provide a baseline for future research. The objective of this paper is to conduct a thorough bibliometric analysis of the literature on mobile learning in vocational high schools. Articles were obtained from the Scopus database, as many as 121 articles were studied in this study, out of 132 articles discovered between 2015-2020. After managing the database, the researcher used VOSviewer software to classify and visualize co-authorship, co-accuracy, and citation. Overall, the information presented in this review can be used as a starting point for future research on the topic of m - learning in educational process of vocational high school.

This paper synthesizes the characteristics of university rankings that cause pressure or 'harassment' on universities and analyses possible strategies for action that can be carried out in the real context of the three main university rankings (QS, THE, ARWU), suggesting the consequences for the so-called world-class universities and others (which represent 95% of the total). Although there is controversy over the criteria of the university ranking systems, many universities are establishing strategies aimed at adapting to these criteria and indicators and improving their positions. This study concludes that international university rankings should not be a relevant source of information to consider the quality of universities because this can have a negative impact on the development of medium- and long-term policies in higher education and the universities themselves. Universities should concentrate on their mission and must provide valid and reliable information to all stakeholders about the level of achievement of their goals.

This paper is based on statistical and qualitative analysis of library and information science (LIS) literature. Our study asks the question of whether, and if so, how, and how often, the discourse generated by scholarly literature in LIS engages discussion about identity in LIS, what topics are covered, and whether the articles engage praxis, or the application of theory to practice. Through searches in LISTA database that cross-referenced identity terms (e.g., Gender, LGBT) with terms describing prominent areas in the LIS field, we collected quantitative data and analyzed the co-occurrence of keywords and created network visualizations. We used a qualitative coding scheme to rate abstracts in terms of their relevance and actionability. Both modes of analysis show that generalized conversations about identity are most common, and praxis is rarely achieved. Using critical approaches to LIS, we explore the ramifications of silences within LIS literature upon the pedagogical and professional discourse.

SQL query writing is a challenging task for novices, even after considerable training. Query writing is a programming task and a translation task where the writer must translate a user's request for information into code that conforms to the structure, constraints, and syntax of an SQL SELECT statement and that references specific tables and columns from a database. This paper investigates the impact of two instructional interventions on query errors under conditions of low and high query complexity. Data was collected from an experimental study of 63 undergraduate students nearing completion of a 15-week database course. Our analysis reveals specific areas of query writing where each of the interventions helped, and hindered, task performance. We discuss the implications of these findings for improving SQL training and for future research on SQL training effectiveness.

This paper describes the use and benefits of Microsoft's Adventure Works (AW) database to teach advanced database skills in a hands-on, realistic environment. Database management and querying skills are a key element of a robust information systems curriculum, and active learning is an important way to develop these skills. To facilitate active learning and produce a compelling narrative, the data structure and content of a useful pedagogically-oriented database should be realistic and lifelike. It should contain data that accurately depicts the business processes, functions, and entities of a realistic organization, structured in a way that demonstrates best practices in database design. Most database textbooks include sample databases, but these are often small and sparse of data. By contrast, Microsoft's AW database presents a robust, realistic, and comprehensive framework for many important educational objectives in an IS curriculum. This paper introduces the AW business case and database, and illustrates several pedagogical uses in an undergraduate CIS program.

Boolean logic can be a difficult concept for first-year, introductory students to grasp. This paper compares the results of Boolean and natural language searching across several databases with searches created from student research questions. Performance differences between databases varied. Overall, natural search language is at least as good as Boolean searching. With evidence that students struggle to grasp Boolean searching, and may not use it even after instruction, it could be left out of first-year instruction, freeing up valuable class time to focus on concepts such as question development and source evaluation. As the Framework for Information Literacy does not specifically address Boolean operators, the authors suggest it should have less prominence in first-year Information Literacy instruction.

Many organizations are dealing with the increasing demands of big data, so they are turning to NoSQL databases as their preferred system for handling the unique problems of capturing and storing massive amounts of data. Therefore, it is likely that employees in all sizes of organizations will encounter NoSQL databases. Thus, to be more job-ready, college students need to be introduced to this technology to begin to have a functional understanding of how it works and how to use it. This paper provides a simple project-based, teaching case that introduces NoSQL and can be easily integrated into any existing database management course to augment concepts and skills geared around traditional SQL relational databases. The teaching case was tested and student feedback (pre- and post-assessment results, shown in the data analytics and results section) indicated a significant increase in their basic knowledge of NoSQL.

Due to the similarity and diversity among kinases, small molecule kinase inhibitors (SMKIs) often display multi-target effects or selectivity, which have a strong correlation with the efficacy and safety of these inhibitors. However, due to the limited number of well-known popular databases and their restricted data mining capabilities, along with the significant scarcity of databases focusing on the pharmacological similarity and diversity of SMIKIs, researchers find it challenging to quickly access relevant information. The KLIFS database is representative of specialized application databases in the field, focusing on kinase structure and co-crystallised kinase-ligand interactions, whereas the KLSD database in this paper emphasizes the analysis of SMKIs among all reported kinase targets. To solve the current problem of the lack of professional application databases in kinase research and to provide centralized, standardized, reliable and efficient data resources for kinase researchers, this paper proposes a research program based on the ChEMBL database. It focuses on kinase ligands activities comparisons. This scheme extracts kinase data and standardizes and normalizes them, then performs kinase target difference analysis to achieve kinase activity threshold judgement. It then constructs a specialized and personalized kinase database platform, adopts the front-end and back-end separation technology of SpringBoot architecture, constructs an extensible WEB application, handles the storage, retrieval and analysis of the data, ultimately realizing data visualization and interaction. This study aims to develop a kinase database platform to collect, organize, and provide standardized data related to kinases. By offering essential resources and tools, it supports kinase research and drug development, thereby advancing scientific research and innovation in kinaserelated fields. [ABSTRACT FROM AUTHOR]

Purpose: The expansion of research across various disciplines has led to a substantial increase in published papers and journals, highlighting the necessity for reliable text mining platforms for database construction and knowledge acquisition. This abstract introduces GPDMiner(Gene, Protein, and Disease Miner), a platform designed for the biomedical domain, addressing the challenges posed by the growing volume of academic papers. Methods: GPDMiner is a text mining platform that utilizes advanced information retrieval techniques. It operates by searching PubMed for specific queries, extracting and analyzing information relevant to the biomedical field. This system is designed to discern and illustrate relationships between biomedical entities obtained from automated information extraction. Results: The implementation of GPDMiner demonstrates its efficacy in navigating the extensive corpus of biomedical literature. It efficiently retrieves, extracts, and analyzes information, highlighting significant connections between genes, proteins, and diseases. The platform also allows users to save their analytical outcomes in various formats, including Excel and images. Conclusion: GPDMiner offers a notable additional functionality among the array of text mining tools available for the biomedical field. This tool presents an effective solution for researchers to navigate and extract relevant information from the vast unstructured texts found in biomedical literature, thereby providing distinctive capabilities that set it apart from existing methodologies. Its application is expected to greatly benefit researchers in this domain, enhancing their capacity for knowledge discovery and data management. [ABSTRACT FROM AUTHOR]

Detecting instruments in a music signal is often used in database indexing, song annotation, and creating applications for musicians and music producers. Therefore, effective methods that automatically solve this issue need to be created. In this paper, the mentioned task is solved using mel-frequency cepstral coefficients (MFCC) and various architectures of artificial neural networks. The authors' contribution to the development of automatic instrument detection covers the methods used, particularly the neural network architectures and the voting committees created. All these methods were evaluated, and the results are presented and discussed in the paper. The proposed automatic instrument detection methods show that the best classification quality was obtained for an extensive model, which is the so-called committee of voting classifiers. [ABSTRACT FROM AUTHOR]

This paper presents a method for developing a malware ontology structure by detecting malware instances on Twitter. The ontology represents a semi-automatic classifier fed by the data extracted from tweets. In particular, the automatic part of the presented methodology relies on a pattern-based approach to detect trigger expressions leading to new information about malware, whilst the manual one covers the evaluation of the results by domain-experts, who also validate the reliability of the semantic relationships within the ontology framework. We present preliminary results on the application of our methodology to tweets extracted from MalwareBazaar database showing how the documents’ collection analysis, through Natural Language Processing (NLP) tasks, can support the knowledge retrieval and documents’ classification procedures for building early warning system of detected malware. Results obtained from this research paper within the time framework of 2023 are referred to the previous version of the current social network X. [ABSTRACT FROM AUTHOR]

Implementing a cloud-based data warehouse to store sensitive or critical strategic data presents challenges primarily related to the security of the stored information and the exchange of OLAP queries between the cloud server and users. Although encryption is a viable solution for safeguarding outsourced data, applying it to OLAP queries involving multidimensional data, measures, and Multidimensional Expressions (MDX) operations on encrypted data poses difficulties. Existing searchable encryption solutions are inadequate for handling such complex queries, which complicates the use of business intelligence tools that rely on efficient and secure data processing and analysis.This paper proposes a new privacy-preserving cloud data warehouse scheme called SSF-CDW which facilitates a secure and scalable solution for an encrypted cloud data warehouse. Our SSF-CDW improves the OLAP queries accessible only to authorized users who can decrypt the query results with improved query performance compared to traditional OLAP tools. The approach involves utilizing symmetric encryption and Ciphertext Policy Attribute-Based Encryption (CP-ABE) to protect the privacy of the dimension and fact data modeled in Multidimensional OLAP (MOLAP). To support efficient OLAP query execution, we proposed a new data cube retrieval mechanism using a Redis schema which is an in-memory database. This technique dynamically compiles queries by disassembling them down into multiple levels and consolidates the results mapped to the corresponding encrypted data cube. The caching of dimensional and fact data associated with the encrypted cube is also implemented to improve the speed of frequently queried data. Experimental comparisons between our proposed indexed search strategy and other indexing schemes demonstrate that our approach surpasses alternative techniques in terms of search speed for both ad-hoc and repeated OLAP queries, all while preserving the privacy of the query results. [ABSTRACT FROM AUTHOR]

Nursing students can access massive amounts of online health data to drive cutting-edge evidence-based practice in clinical placement, to bridge the theory–practice gap. This activity requires investigation to identify the strategies nursing students apply to evaluate online health information. Online Think-Aloud sessions enabled 14 participants to express their cognitive processes in navigating various educational resources, including online journals and databases, and determining the reliability of sources, indicating their strategies for information-seeking, which helped to create this scoring system. Easy access and user convenience were clearly the instrumental factors in this behavior, which has troubling implications for the lack of use of higher-quality resources (e.g., from peer-reviewed academic journals). The identified challenges encountered during resource access included limited skills in the critical evaluation of information credibility and reliability, signaling a requirement for improved information literacy skills. Participants acknowledged the importance of evidence-based, high-quality information, but faced numerous barriers, such as restricted access to professional and specialty databases, and a lack of academic skills training. This paper develops and critiques a Performative Tool for assessing the process of seeking health information using an online Think-Aloud method, and explores factors and strategies contributing to evidence-based health information access and utilization in clinical practice, aiming to provide insight into individuals' information-seeking behaviors in online health contexts. [ABSTRACT FROM AUTHOR]

Background: Libraries provide access to databases with auto-cite features embedded into the services; however, the accuracy of these auto-cite buttons is not very high in humanities and social sciences databases. Case Presentation: This case compares two biomedical databases, Ovid MEDLINE and PubMed, to see if either is reliable enough to confidently recommend to students for use when writing papers. A total of 60 citations were assessed, 30 citations from each citation generator, based on the top 30 articles in PubMed from 2010 to 2020. Conclusions: Error rates were higher in Ovid MEDLINE than PubMed but neither database platform provided error-free references. The auto-cite tools were not reliable. Zero of the 60 citations examined were 100% correct. Librarians should continue to advise students not to rely solely upon citation generators in these biomedical databases. [ABSTRACT FROM AUTHOR]

Blockchain technology has been successfully applied in recent years to promote the immutability, traceability, and authenticity of previously collected and stored data. However, the amount of data stored in the blockchain is usually limited for economic and technological issues. Namely, the blockchain usually stores only a fingerprint of data, such as the hash of data, while full, raw information is stored off-chain. This is generally enough to guarantee immutability and traceability, but misses to support another important property, that is, data availability. This is particularly true when a traditional, centralized database is chosen for off-chain storage. For this reason, many proposals try to properly combine blockchain with decentralized IPFS storage. However, the storage of data on IPFS could pose some privacy problems. This paper proposes a solution that properly combines blockchain, IPFS, and encryption techniques to guarantee immutability, traceability, availability, and data privacy. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of computer storage capabilities and network transmission capabilities, users can easily share their own video and image information on social networking sites, and the amount of multimedia data on the network is rapidly increasing. With the continuous increase of the amount of data in the network, the establishment of effective automated data management methods and search methods has become an increasingly urgent need. This paper proposes a retrieval method of human motion data based on motion capture in index space. By extracting key frames from the original motion to perform horizontal dimensionality reduction and defining features based on Laban motion analysis, the motion segment is subjected to vertical feature dimensionality reduction. After extracting features from the input motion segment, motion matching is performed on the index space. This paper designs the optimization method of the phased dynamic time deformation algorithm in time efficiency and analyzes the optimization method of the phased dynamic time deformation algorithm in time complexity. Considering the time efficiency redundancy, this paper optimizes the time complexity of the phased dynamic time deformation method. This improves the time efficiency of the staged dynamic time warping algorithm, making it suitable for larger-scale human motion data problems. Experiments show that the method in this paper has the advantage of speed, is more in line with the semantics of human motion, and can meet the retrieval requirements of human motion databases. [ABSTRACT FROM AUTHOR]

For many years now, the mining industry has seen a boost in exploring and developing the systems for monitoring operational parameters of mining machines, in particular load-haul-dump machines. Therefore, further researches on algorithmics have also advanced dynamically regarding effective performance management as well as predictive maintenance. Nonetheless, the issue of road conditions is still being neglected. That issue has a substantial impact on both the overall operator's convenience, their performance, and machinery reliability, especially its construction node and tire damages. Moreover, such negligence pertains also to the maintenance of mine infrastructure, including the network of passages. The paper explains the use of the portable inertial measurement unit (IMU) in evaluating road conditions in the deep underground mine. The detailed descriptions of the road quality classification procedure and bump detection have been included. The paper outlines the basic method of tracking the motion trajectory of vehicles and suggests the method of visualization of the results of the road conditions evaluation. This paper covers the sample results collected by the measurements unit in the deep underground mine during six experiments. This paper is an extended version of a paper presented at the ACIIDs 2020 conference [P. Stefaniak, D. Gawelski, S. Anufriiev and P. Śliwiński, Road-quality classification and motion tracking with inertial sensors in the deep underground mine, Asian Conference on Intelligent Information and Database Systems, March 2020, Springer, Singapore, pp. 168–178]. [ABSTRACT FROM AUTHOR]

Cultural Heritage (CH) management software represents virtual information in various ways aiming either at usability and long-term preservation or interactivity and immersiveness. A single web-based framework that couples the organization of geospatial, multimedia and relational data with 4D visualization, Virtual Reality (VR) and Augmented Reality (AR) is presented in this paper (https://meteora.topo.auth.gr/5dmeteora.php). It comprises the 5dMeteora platform and the Content Management System (CMS) for uploading, processing, publishing and updating its content. The 5dMeteora platform integrates a responsive 3D viewer of high-resolution models in the basis of 3DHOP (3D Heritage Online Platform) and Nexus.js multi-resolution library. It offers data retrieval and interpretation mechanisms through navigation tools, clickable geometries in the 3D scene, named hotspots, and semantic organization of metadata. Its content and interactive services are differentiated, based on the scientific specialty or the field of interest of the users. To achieve the sense of spatial presence, VR and AR viewports are designed to give a clearer understanding of spatial bounds and context of 3D CH assets. The proposed CMS allows dynamic content management, automation of 3DHOP's operations regarding 3D data uploading and hotspots defining, real-time preview of the 3D scene as well as extensibility at all levels (e.g., new data types). It is built upon a MySQL Database Management System and developed with PHP scripting, backend JavaScript and Ajax controllers as well as front-end web languages. The database maintains and manages the entities of every type of data supported by the platform, while encryption methods guarantee data confidentiality and integrity. The presented work is the first valid attempt of open-source software that automates the dissemination of 3D and 2D content for customized eXtented Reality (XR) experiences and reaches multiple levels of interactivity for different users (experts, non-experts). It can meet the needs of domain experts that own or manage multimodal heritage data. [ABSTRACT FROM AUTHOR]

The computational complexity of privacy information retrieval protocols is often linearly related to database size. When the database size is large, the efficiency of privacy information retrieval protocols is relatively low. This paper designs an effective privacy information retrieval model based on hybrid fully homomorphic encryption. The assignment method is cleverly used to replace a large number of homomorphic encryption operations. At the same time, the multiplicative homomorphic encryption scheme is first used to deal with the large-scale serialization in the search, and then the fully homomorphic encryption scheme is used to deal with the remaining simple operations. The depth of operations supported by the fully homomorphic scheme no longer depends on the size of the database, but only needs to support the single homomorphic encryption scheme to decrypt the circuit depth. Based on this hybrid homomorphic encryption retrieval model, the efficiency of homomorphic privacy information retrieval model can be greatly improved. [ABSTRACT FROM AUTHOR]

Hospitals produce a large amount of medical information every day. In the face of medical big data, the existing data processing methods cannot meet expectations and need to be continuously optimized. In the database system, when the stored objects are very large, and then the efficiency of data retrieval is a major bottleneck, therefore restricting the application of medical information. For that reason and to improve the efficiency of information retrieval, it is necessary to add an index to the information object and filter the dataset participating in the connection retrieval through the index. In this paper, an information retrieval technique grounded on the R-tree clustering model index is proposed for massive hospital information. The R-tree clustering model is constructed in massive hospital information by using the dynamic determination clustering center (DCC) algorithm. Finally, the superiority of the method is proved by simulations. The experiments and empirical evaluation show that the proposed R-tree clustering model index significantly improves data retrieval efficiency. [ABSTRACT FROM AUTHOR]

Introduction: Observational data are increasingly seen as a valuable source for integrated care research. Especially since the growing availability of routinely collected data and quasi-experimental methods. The aim of this paper is to describe the potentials and challenges when using observational data for integrated maternity care research, based on our experience from developing and working with the Data-InfrAstructure for ParEnts and childRen (DIAPER). Methods and Results: We provide a description of DIAPER, which is a linked datainfrastructure on the individual level based on maternity care claims data, quality and utilization of maternity care and data from municipal registries, covering the life course from preconception to adulthood. We then discuss potentials and practical applications of DIAPER such as to evaluate alternative payment models for integrated maternity care, to set the policy agenda regarding postpartum care, to provide insights into value of care and into provider variation, and to evaluate (policy) interventions designed to promote and support integrated maternity care. This is relevant for several stakeholders: policy makers, payers, providers and clients/patients. Based on experiences with DIAPER, we identify remaining challenges: missing data sources (especially self-reported outcomes), suboptimal quality of data, privacy concerns and potential biases introduced during data linkage, and describe how these challenges were tackled within the applications of DIAPER. Conclusions: With DIAPER we demonstrated that using observational data can be of added value for integrated care research, but also that challenges remain. It is essential to keep exploring and developing the possibilities of observational data and continue the discussions in the scientific community. Learning from each other's successes and failures will be critical. [ABSTRACT FROM AUTHOR]

Knowledge graph is a kind of semantic network for information retrieval. How to construct a knowledge graph that can serve the power system based on the behavior data of dispatchers is a hot research topic in the area of electric power artificial intelligence. In this paper, we propose a method to construct the dispatch knowledge graph for the power grid. By leveraging on dispatch data from the power domain, this method first extracts entities and then identifies dispatching behavior relationship patterns. More specifically, the method includes three steps. First, we construct a corpus of power dispatching behaviors by semi-automated labeling. And then, we propose a model, called the BiLSTM-CRF model, to extract entities and identify the dispatching behavior relationship patterns. Finally, we construct a knowledge graph of power dispatching data. The knowledge graph provides an underlying knowledge model for automated power dispatching and related services and helps dispatchers perform better power dispatch knowledge retrieval and other operations during the dispatch process. [ABSTRACT FROM AUTHOR]

With natural language processing as an important research direction in deep learning, the problems of text similarity calculation, natural language inference, question and answer systems, and information retrieval can be regarded as text matching applications for different data and scenarios. Secure matching computation of text string patterns can solve the privacy protection problem in the fields of biological sequence analysis, keyword search, and database query. In this paper, we propose an Intelligent Semi-Honest System (ISHS) for secret matching against malicious adversaries. Firstly, a secure computation protocol based on the semi-honest model is designed for the secret matching of text strings, which adopts a new digital encoding method and an ECC encryption algorithm and can provide a solution for honest participants. The text string matching protocol under the malicious model which uses the cut-and-choose method and zero-knowledge proof is designed for resisting malicious behaviors that may be committed by malicious participants in the semi-honest protocol. The correctness and security of the protocol are analyzed, which is more efficient and has practical value compared with the existing algorithms. The secure text matching has important engineering applications. [ABSTRACT FROM AUTHOR]

Quantifying the dissimilarity of two texts is an important aspect of a number of natural language processing tasks, including semantic information retrieval, topic classification, and document clustering. In this paper, we compared the properties and performance of different dissimilarity measures D using three different representations of texts—vocabularies, word frequency distributions, and vector embeddings—and three simple tasks—clustering texts by author, subject, and time period. Using the Project Gutenberg database, we found that the generalised Jensen–Shannon divergence applied to word frequencies performed strongly across all tasks, that D's based on vector embedding representations led to stronger performance for smaller texts, and that the optimal choice of approach was ultimately task-dependent. We also investigated, both analytically and numerically, the behaviour of the different D's when the two texts varied in length by a factor h. We demonstrated that the (natural) estimator of the Jaccard distance between vocabularies was inconsistent and computed explicitly the h-dependency of the bias of the estimator of the generalised Jensen–Shannon divergence applied to word frequencies. We also found numerically that the Jensen–Shannon divergence and embedding-based approaches were robust to changes in h, while the Jaccard distance was not. [ABSTRACT FROM AUTHOR]

A private proximity retrieval (PPR) scheme is a protocol which allows a user to retrieve the identities of all records in a database that are within some distance $r$ from the user’s record $x$. The user’s privacy at each server is given by the fraction of the record $x$ that is kept private. In this paper, this research is initiated and protocols that offer trade-offs between privacy, computational complexity, and storage are studied. In particular, we assume that each server stores a copy of the database and study the required minimum number of servers by our protocol which provides a given privacy level. Each server receives a query in the protocol and the set of queries forms a code. The main focus in the paper is dedicated to studying the family of codes generated by the set of queries. These codes will be shown to satisfy a specific covering property and will be called private proximity retrieval intersection covering codes. In particular, since the query every server receives is a codeword, the goal is to minimize the number of codewords in such a code which is the minimum number of servers required by the protocol. These codes are closely related to a family of codes known as covering designs. We introduce several lower bounds on the sizes of such codes as well as several constructions. This work focuses on the case when the records are binary vectors together with the Hamming distance. Other metrics such as the Johnson metric are also investigated. [ABSTRACT FROM AUTHOR]

This paper proposes a novel approach, EA2-IMDG (Efficient Approach of Using an In-Memory Data Grid) to improve the performance of replication and scheduling in grid environment systems. Grid environments are widely used for distributed computing, but they are often faced with the challenge of high data access latency and poor scalability. By utilizing an in-memory data grid (IMDG), the aim is to significantly reduce the data access latency and improve the resource utilization of the system. The approach uses the IMDG to store data in RAM, instead of on disk, allowing for faster data retrieval and processing. The IMDG is used to distribute data across multiple nodes, which helps to reduce the risk of data bottlenecks and improve the scalability of the system. To evaluate the proposed approach, a series of experiments were conducted, and its performance was compared with two baseline approaches: a centralized database and a centralized file system. The results of the experiments show that the EA2-IMDG approach improves the performance of replication and scheduling tasks by up to 90% in terms of data access latency and 50% in terms of resource utilization, respectively. These results suggest that the EA2-IMDG approach is a promising solution for improving the performance of grid environment systems. [ABSTRACT FROM AUTHOR]

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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. [ABSTRACT FROM AUTHOR]

The digital transformation sets new requirements to all classes of enterprise systems in companies. ERP systems in particular, which represent the dominant class of enterprise systems, are struggling to meet the new requirements at all levels of the architecture. Therefore, there is an urgent need to reconsider the overall architecture of the systems and address the root of the related issues. Given that many restrictions ERP pose on their adaptability are related to the standardization of data, the database layer of ERP systems is addressed. Since database serve as the foundation for data storage and retrieval, they limit the flexibility of enterprise systems and the chance to adapt to new requirements accordingly. So far, relational databases are widely used. Using a systematic literature approach, recent requirements for ERP systems were identified. Prominent database approaches were assessed against the 23 requirements identified. The results reveal the strengths and weaknesses of recent database approaches. To this end, the results highlight the demand to combine multiple database approaches to fulfill recent business requirements. From a conceptual point of view, this paper supports the idea of federated databases which are interoperable to fulfill future requirements and support business operation. This research forms the basis for renewal of the current generation of ERP systems and proposes to ERP vendors to use different database concepts in the future. [ABSTRACT FROM AUTHOR]

Wafer map defect pattern contains the critical information about semiconductor manufacturing, effective defect analysis technology can improve the yield of products. This paper develops a wafer defect pattern recognition and analysis method based on convolutional neural network (CNN). We build an 8-layer CNN model to inspect wafer map defect and a 13-layer model to classify defect patterns. To analyze the defects, we first extract features based on the classification network, then perform PCA dimensionality reduction and similarity ranking, we infer the root causes of tested samples according to the retrieved wafer maps. In particular, we analyze the impacts of different network layers and feature dimensions on image retrieval performance, it turns out that the appropriate dimensionality reduction can increase the accuracy and speed of wafer map retrieval. The models are trained and tested on the WM-811K wafer map dataset, which are collected from real wafer maps. The experimental results show that the proposed method is able to identify common wafer defect patterns and analyze the root causes accurately and effectively. [ABSTRACT FROM AUTHOR]

The International Whaling Commission’s (IWC) Database‐Estimation and Software System (DESS) was designed to standardise IWC line‐transect survey data storage and streamline abundance estimation, including abundance estimation by small management area to inform decision making. Not only was DESS designed to accommodate all International Decade of Cetacean Research (IDCR) and Southern Ocean Whale and Ecosystem Research (SOWER) surveys, but also national surveys, such as the Japanese surveys in the Antarctic and North Pacific, and multi‐national surveys conducted in the North Atlantic. The standard software for estimating abundance was program Distance, which could estimate abundance in designed survey blocks, however, management procedures required estimation not just in survey blocks but also in smaller regions that could be defined by the user. This added a spatial component to data selection which led to the inclusion of a geographical information system (GIS). This paper describes the data stored in DESS and the features available for data retrieval and analysis. [ABSTRACT FROM AUTHOR]

The increasing amount of graph-structured data catalyzes analytics over graph databases using semantic queries. Motivated by the ubiquity of commercial cloud platforms, data owners are willing to store their graph databases remotely. However, data privacy has emerged as a widespread concern since the cloud platforms are not fully trusted. One viable solution is to encrypt sensitive data before outsourcing, which inevitably hinders data retrieval. To enable queries over encrypted data, searchable symmetric encryption (SSE) has been introduced. Yet, the most well-studied class of SSE schemes focuses on retrieving textual files given keywords, which cannot be applied to graph databases directly. This paper extends our preliminary work (FC’17) and proposes GraphShield, a structured encryption scheme for graphs. Beyond shortest distance queries, GraphShield can support other classic graph-based queries (e.g., maximum flow) and more complicated analytics (e.g., PageRank). Technically, we incorporate a suite of (efficient) cryptographic primitives and tailor some extra secure protocols for facilitating graph analytics. Our scheme also allows updates on the encrypted graph with forward privacy guaranteed. We formalize the security model and prove the adaptive security with reasonable leakage. Finally, we implement our scheme on various real-world datasets, and the experiment results demonstrate its practicality and scalability. [ABSTRACT FROM AUTHOR]

Background: Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. Methods: The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver. Results: From the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect. Conclusion: The Bio-DTM model not only discovers what ginseng's research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies. [ABSTRACT FROM AUTHOR]

Support for efficient spatial data storage and retrieval has become a vital component in almost all spatial database systems. While GPUs have become a mainstream platform for high-throughput data processing in recent years, exploiting the massively parallel processing power of GPUs is non-trivial. Current approaches that parallelize one query at a time have low work efficiency and cannot make good use of GPU resources. On the other hand, many spatial database systems could receive a large number of queries simultaneously. In this paper, we present a comprehensive framework named G-PICS for parallel processing of concurrent spatial queries on GPUs. G-PICS encapsulates efficient parallel algorithms for constructing a variety of spatial trees with different space partitioning methods. G-PICS also provides highly optimized programs for processing major spatial query types, and such programs can be accessed via an API that could be further extended to implement user-defined algorithms. While support for dynamic data inputs is missing in existing work, G-PICS implements efficient parallel algorithms for bulk updates of data. Furthermore, G-PICS is designed to work in a Multi-GPU environment to support datasets beyond the size of a single GPU’s global memory. Empirical evaluation of G-PICS shows significant performance improvement over the state-of-the-art GPU and parallel CPU-based spatial query processing systems. In particular, G-PICS achieves double-digit speedup over such systems in tree construction (up to 53X) and query processing (up to 80X). [ABSTRACT FROM AUTHOR]

This paper has conducted a contrastive study on the AIRS retrieval results and the observational data of methane concentration at Mt. Waliguan atmospheric background station, and has analyzed the distribution characteristics of atmospheric methane concentration over the Qinghai-Xizang Plateau from 2003 to 2015. The results show that the AIRS retrieval data display the same monthly, annual, and seasonal variation trend, as well as segmental variation characteristics. The methane concentration features high in the north and low in the south along with the geographical line of Kunlun Mountains–Tanggula Mountains–Hengduan Mountains, and decreases significantly as the altitude rises, with the highest and the most sensitive variability in the south central region of the Qinghai-Xizang Plateau. From 2003 to 2015, the methane concentration in the Qinghai-Xizang Plateau continued to rise, with the fastest growth in autumn, the slowest in winter, and an annual growth rate of 5.2 nmol mol−1 a−1, while the growth rate from 2013 to 2015 was lower than the global average. The seasonal variation showed a unimodal curve, with the highest value in summer and the lowest value in spring, and with the altitude rising, the seasonal variation was more significant. [ABSTRACT FROM AUTHOR]

Private information retrieval (PIR) protocols allow a user to retrieve entries of a database without revealing the index of the desired item. Information-theoretical privacy can be achieved by the use of several servers and specific retrieval algorithms. Most known PIR protocols focus on decreasing the number of bits exchanged between the client and the server(s) during the retrieval process. On another side, Fazeli et al. introduced so-called PIR codes in order to reduce the storage overhead on the servers. However, few works address the issue of the computation complexity of the servers. In this paper, we show that a specific encoding of the database yields PIR protocols with reasonable communication complexity, low storage overhead, and optimal computational complexity for the servers. This encoding is based on incidence matrices of transversal designs, from which a natural and efficient recovering algorithm is derived. We also present several instances for our construction, which make use of finite geometries and orthogonal arrays. We finally give a generalization of our main construction in order to resist collusions of servers. [ABSTRACT FROM AUTHOR]

The $T$ -Private Information Retrieval ($T$ -PIR) problem is that a user wishes to retrieve a single record from $N$ servers, without revealing the identity of the desired record even if the number of colluding servers up to $T$. Here every server stores all $M$ records and each record can be viewed as a vector over the $q$ -ary finite field $\mathbb {F}_{q}$. To reduce the field size, a capacity-achieving $T$ -PIR scheme based on some MDS array codes for all possible $N>T,M\geq 2$ was constructed by Xu and Zhang in 2019. A key idea in that scheme is to construct some MDS array codes in query phase, whose generator matrices satisfy the recovery property (see (C1) in , page 6), i.e., there exist some specific columns in each generator matrix such that the number of them is equal to the rank of such matrix. We call the set of each column index of those columns in each generator matrix as the column index set. However, that scheme didn’t give an explicit selection of the column index set satisfying the recovery property. In this paper, let $N=d(2t-1)$ , $N>T=dt>1, M\geq 3$ and $d\geq 1$ , under the constraint of $2^{t-1}\geq N$ , we present a novel method to construct capacity-achieving $T$ -PIR schemes over the binary field with an explicit selection of the column index set. Moreover, our scheme has sub-packetization $N(2t-1)^{M-2}$ , which is optimal for all linear capacity-achieving $T$ -PIR schemes determined by Zhang and Xu in 2019. The key to our method is that all locators and column multipliers are precisely selected such that a Generalized Reed-Solomon (GRS) code defined by them has a zero-dimensional subfield sub-code. Compared with all the known capacity-achieving $T$ -PIR schemes for the same non-trivial parameters, ours is the first explicit capacity-achieving $T$ -PIR scheme over the binary field. [ABSTRACT FROM AUTHOR]

The fundamental limits of private information retrieval (PIR) with unknown cache prefetching at the user are investigated in this paper. To this end, a novel random linear combination (RLC)-based PIR scheme that can solve the basic PIR problem and its variation is proposed. The proposed scheme is based on random coding approach and achieves the capacity of the basic PIR asymptotically. Then, we investigate PIR with unknown cache prefetching (PIRC) problem at different cache-to-file size ratio. Specifically, we propose RLC-based PIRC method, which prefetches RLC-based side information and leverages them to retrieve desired information at small download cost. Furthermore, by applying time and memory sharing on the proposed RLC-based PIRC, RLC-based basic PIR and some other known approach in literature, we derive the achievable normalized download cost bound of PIRC. The derived achievable bound outperforms the existing bound in literature and the case study provides numerical results that verifies it. [ABSTRACT FROM AUTHOR]

Modern very large-scale integration (VLSI) layout databases routinely consist of 10e15 edges, and thus problems of information retrieval, intellectual property (IP) inventory control, tampering detection, IP infringement detection, data tagging, and database version control, are extremely computationally intensive. All these tasks can be reduced to the problem of copy detection, and in this paper, we propose a canonical hash function for VLSI layout datasets which can be used for efficient copy detection and signature generation. The proposed signature is independent of the ordering of the layout elements, their tessellation, resolution, and even vertex count. These parameters, which do not contribute to the final wafer image, increase the entropy of the data and thus standard hash functions such as message digest (MD5) or secure hash algorithm (SHA), are not suitable for this problem of VLSI layout hashing. In this paper, a novel, entropy reduced hash function is developed which can be used to alleviate the above mentioned problems of physical IP management. The proposed method has O(n \log n + k) time complexity, and O(\sqrt n) memory complexity, where n is the number of edges in the input layout, and k is the number of intersections between edges. The proposed system has been implemented, and computational results validating our approach are also provided. [ABSTRACT FROM AUTHOR]

We consider the problem of cache-aided Multiuser Private Information Retrieval (MuPIR) which is an extension of the single-user cache-aided PIR problem to the case of multiple users. In cache-aided MuPIR, each of the $K_{\mathrm{ u}}$ cache-equipped users wishes to privately retrieve a message out of $K$ messages from $N$ databases each having access to the entire message library. Demand privacy requires that any individual database learns nothing about the demands of all users. The users are connected to each database via an error-free shared-link. In this paper, we aim to characterize the optimal trade-off between user cache memory and communication load for such systems. First, we propose a novel approach of cache-aided interference alignment (CIA), for the MuPIR problem with $K=2$ messages, $K_{\mathrm{ u}}=2$ users and $N\ge 2$ databases. The CIA approach is optimal when the cache placement is uncoded. For general cache placement, the CIA approach is optimal when $N=2$ and 3 verified by the computer-aided converse approach. Second, for the general case, we propose a product design (PD) which incorporates the PIR code into the linear caching code. The product design is shown to be order optimal within a multiplicative factor of 8 and is exactly optimal in the high memory regime. [ABSTRACT FROM AUTHOR]

Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (SPIR) is a variant where a user is able to privately retrieve one out of K messages from N non-colluding replicated databases without learning anything about the remaining K−1 messages. However, the goal of perfect privacy can be too taxing for certain applications. In this paper, we investigate if the information-theoretic capacity of SPIR (equivalently, the inverse of the minimum download cost) can be increased by relaxing both user and DB privacy definitions. Such relaxation is relevant in applications where privacy can be traded for communication efficiency. We introduce and investigate the Asymmetric Leaky PIR (AL-PIR) model with different privacy leakage budgets in each direction. For user privacy leakage, we bound the probability ratios between all possible realizations of DB queries by a function of a non-negative constant ε. For DB privacy, we bound the mutual information between the undesired messages, the queries, and the answers, by a function of a non-negative constant &#948. We propose a general AL-PIR scheme that achieves an upper bound on the optimal download cost for arbitrary ε and δ. We show that the optimal download cost of AL-PIR is upper-bounded as D*(ε,δ) ≤ 1+ 1/N−1 − δeε/NK−1−1. Second, we obtain an information-theoretic lower bound on the download cost as D*(ε,δ) ≥ 1 + 1/Neε−1 − δ/(Neε)K−1 −1. The gap analysis between the two bounds shows that our AL-PIR scheme is optimal when ε = 0, i.e., under perfect user privacy and it is optimal within a maximum multiplicative gap of N−e−ε/N−1 for any ε > 0 and δ > 0. [ABSTRACT FROM AUTHOR]

As warehouse data volumes expand, single-node solutions can no longer analyze the immense volume of data. Therefore, it is necessary to use shared nothing architectures such as MapReduce. Inter-node data segmentation in MapReduce creates node connectivity issues, network congestion, improper use of node memory capacity and inefficient processing power. In addition, it is not possible to change dimensions and measures without changing previously stored data and big dimension management. In this paper, a method called Atrak is proposed, which uses a unified data format to make Mapper nodes independent to solve the data management problem mentioned earlier. The proposed method can be applied to star schema data warehouse models with distributive measures. Atrak increases query execution speed by employing node independence and the proper use of MapReduce. The proposed method was compared to established methods such as Hive, Spark-SQL, HadoopDB and Flink. Simulation results confirm improved query execution speed of the proposed method. Using data unification in MapReduce can be used in other fields, such as data mining and graph processing. [ABSTRACT FROM AUTHOR]

Conceptual and relational data models of online transaction processing (OLTP) applications are usually created and maintained following the principle of normalization, which implies avoidance of redundancy. Data retrieval from a disk-based normalized relational database often requires complex and inefficient queries that may cause noticeable performance issues when executed on larger volumes of data. Computer professionals sometimes intentionally trade off the strict normal form to optimize data retrieval queries through error-prone manual tuning and denormalization. We propose a fully automatic optimization approach, based on data redundancy, that relies on a formal cost-benefit model. We prove that finding the optimal level of data redundancy, for given workload statistics, is an NP-Complete optimization problem. A detailed reduction of the problem to binary linear programming is presented in the paper. The proposed optimization approach was evaluated using the TPCE benchmark for OLTP systems. The evaluation has shown that the proposed optimization approach is highly scalable, and that it can be efficiently applied to real-life relational data models. [ABSTRACT FROM AUTHOR]

The problem of private information retrieval (PIR) gets renewed attentions in recent years due to its information-theoretic reformulation and applications in distributed storage systems. Let M files be stored in a distributed storage system consisting of N servers, where each file is stored via an (N, K)-MDS code. A PIR scheme will allow a user to retrieve a specific file from the distributed storage system without revealing the identity of the file to any T colluding servers. PIR rate is defined as the number of symbols privately retrieved per one downloaded symbol and the supremum of all achievable rates is called the PIR capacity. The capacity has been solved for some degenerate cases, i.e. K = 1 or T = 1 . For the general case K ≥ 2 and T ≥ 2 , the exact PIR capacity remains unknown. In this paper we propose a general private information retrieval scheme for MDS coded databases with colluding servers achieving PIR rate (1 + R + R 2 + ⋯ + R M - 1) , where R = 1 - N - T K N K . Our scheme captures the essence of the optimal schemes for degenerate cases. We also compare our scheme with some other known PIR schemes for non-degenerate cases. The advantages of our scheme include its independence of the property of the storage code and a better performance when the number of files M is small. [ABSTRACT FROM AUTHOR]

Protein secondary structure describe protein construction in terms of regular spatial shapes, including alpha-helices, beta-strands, and loops, which protein amino acid chain can adopt in some of its regions. This information is supportive for protein classification, functional annotation, and 3D structure prediction. The relevance of this information and the scope of its practical applications cause the requirement for its effective storage and processing. Relational databases, widely-used in commercial systems in recent years, are one of the serious alternatives honed by years of experience, enriched with developed technologies, equipped with the declarative SQL query language, and accepted by the large community of programmers. Unfortunately, relational database management systems are not designed for efficient storage and processing of biological data, such as protein secondary structures. In this paper, we present a new search method implemented in the search engine of the PSS-SQL language. The PSS-SQL allows formulation of queries against a relational database in order to find proteins having secondary structures similar to the structural pattern specified by a user. In the paper, we will show how the search process can be accelerated by multiple scanning of the Segment Index and parallel implementation of the alignment procedure using multiple threads working on multiple-core CPUs. [ABSTRACT FROM AUTHOR]