Complete Query Research Articles

Satisfaction Level of the Farmers Regarding Kisan Call Centre (KCC) Advisories

The Kisan Call Centre (KCC), initiated on January 21st, 2004, by the Department of Agriculture and Cooperation, Ministry of Agriculture, Government of India, serves as a crucial support system for farming communities across India. This study, conducted in 2021-22 in Chamarajanagar District of Karnataka, aimed to assess the Satisfaction Level of 200 KCC beneficiaries regarding its advisory services. Utilizing stratified random sampling, the study employed an ex-post facto research design to gather insights. The data collection involved secondary data from the KCC in Bengaluru and primary data through structured interviews with beneficiaries. Results revealed that 76.00% of beneficiaries expressed high satisfaction with the technical knowledge of KCC experts, with 74.50% satisfied with expert availability, and 73.50% with expert guidance in advisory services. Additionally,70.50% were highly satisfied with patience of expert in listening to the complete query of the farmers through helpline services. Regarding technological information provided by the KCC 53.00% expressed high satisfaction with the technological information provided by the KCC, and 52.50% acknowledged the assistance of advisory services in crop preservation and yield improvement. However, including all the aspect like technical information, helpline services and advisory services provided by the KCC. Notably, a significant 44.00% of both dryland and irrigated land farmers expressed high satisfaction with the overall services of the KCC. This research highlights the importance of ICT-driven initiatives like the KCC in empowering farmers and improving agricultural productivity and sustainability.

Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature.

In recent years, the use of artificial intelligence (AI) has become increasingly popular in dentistry because it facilitates the process of diagnosis and clinical decision-making. However, AI holds multiple prominent drawbacks, which restrict its wide application today. It is necessary for dentists to be aware of AI's pros and cons before its implementation. Therefore, the present study was conducted to comprehensively review various applications of AI in all dental branches along with its advantages and disadvantages. For this review article, a complete query was carried out on PubMed and Google Scholar databases and the studies published during 2010-2022 were collected using the keywords "Artificial Intelligence", "Dentistry," "Machine learning," "Deep learning," and "Diagnostic System." Ultimately, 116 relevant articles focused on artificial intelligence in dentistry were selected and evaluated. In new research AI applications in detecting dental abnormalities and oral malignancies based on radiographic view and histopathological features, designing dental implants and crowns, determining tooth preparation finishing line, analyzing growth patterns, estimating biological age, predicting the viability of dental pulp stem cells, analyzing the gene expression of periapical lesions, forensic dentistry, and predicting the success rate of treatments, have been mentioned. Despite AI's benefits in clinical dentistry, three controversial challenges including ease of use, financial return on investment, and evidence of performance exist and need to be managed. As evidenced by the obtained results, the most crucial progression of AI is in oral malignancies' diagnostic systems. However, AI's newest advancements in various branches of dentistry require further scientific work before being applied to clinical practice. Moreover, the immense use of AI in clinical dentistry is only achievable when its challenges are appropriately managed.

SketchCADGAN: A generative approach for completing partially drawn query sketches of engineering shapes to enhance retrieval system performance

Retrieval systems are commonly used to find relevant data in large datasets. In engineering, these systems are useful for locating specific engineering shapes in a large dataset of engineering components. When end users want to search for a shape, they prefer a two-dimensional (2D) sketch over a three-dimensional (3D) object. However, users lacking domain knowledge may struggle to generate a complete query sketch and provide a partially completed sketch instead. Retrieving relevant information from partially drawn sketches is difficult because they may have missing edges, partially drawn circles, holes, ovals, etc. Most retrieval systems compare the similarity between the query and items in the database, so incomplete sketches may be ineffective in finding the relevant information.To address this problem with incomplete sketches, we propose a new generative adversarial network called SketchCADGAN. This network uses a two-stage cascaded architecture, with the first network attempting to predict a CAD model image from an incomplete sketch and the second network using the CAD model image to predict a completed sketch. Both networks are trained together adversarially. Our approach is proven more effective than other advanced techniques through qualitative and quantitative comparisons. Furthermore, we present the results of the retrieval system using both partially drawn and completed sketches, and demonstrate that incorporating completed sketches from the suggested cascaded GAN architecture results in improved retrieval performance.

INEv: In-Network Evaluation for Event Stream Processing

Complex event processing (CEP) detects situations of interest by evaluating queries over event streams. Once CEP is used in networked applications, the distribution of query evaluation among the event sources enables performance optimization. Instead of collecting all events at one location for query evaluation, sub-queries are placed at network nodes to reduce the data transmission overhead. Yet, existing techniques either place such sub-queries at exactly one node in the network, which neglects the benefits of truly distributed evaluation, or are agnostic to the network structure, which ignores transmission costs due to the absence of direct network links. To overcome the above limitations, we propose INEV graphs for in-network evaluation of CEP queries with rich semantics, including Kleene closure and negation. Our idea is to introduce fine-granular routing of partial results of sub-queries as an additional degree of freedom in query evaluation: We exploit events already disseminated in the network as part of one sub-query, when evaluating another one. We show how to instantiate INEv graphs by splitting a query workload into sub-queries, placing them at network nodes, and forwarding of their results to other nodes. Also, we characterize INEv graphs that guarantee correct and complete query evaluation, and discuss their construction based on a cost model that unifies transmission and processing latency. Our experimental results indicate that INEv graphs can reduce transmission costs for distributed CEP by up to eight orders of magnitude compared to baseline strategies.

Genomic characterisation of three GES-producing Enterobacterales isolated in the Czech Republic

The aim of the study is to characterise the genomic features of three GES-producing Enterobacterales isolates from Czech hospitals. In 2020, during a routine screening of the hospital's surfaces in Prague General Hospital, two strains (CZ862 and CZ863) that belonged to the Enterobacter cloacae complex were found to be blaGES positive. Another blaGES positive strain identified as Klebsiella oxytoca was recovered from a patient hospitalised in Pilsen. Antibiotic susceptibility profiling was done with broth microdilution assay. Conjugation/transformation experiments were performed on all three strains. Genomic DNA of the three isolates was subjected to whole genome sequencing using PacBio platform. Multilocus sequence types typing of CZ862 and CZ863 identified the strains as ST837 and a novel ST (ST1622). Both blaGES harbouring plasmids showed high sequence similarity and complete query coverage (100% and 99.98%) with pEcl-35771cz. Both plasmids had two copies of blaGES instead of one copy as found in pEcl-35771cz. The clinical isolate CZ598 belonged to ST180. The plasmid harboured blaGES-7 gene, cat and aac(6')-lb and the novel variant blaOXA-1011. No similar sequences were observed, suggesting a novel plasmid. The detection of the two blaGES-positive plasmids in the same hospital environment, the first report after 3 years, suggests a hidden source. This highlights the importance of the hidden sources and evolution of such plasmids on the route of spreading into clinical settings. Also, the detection of the new blaOXA-1011, which is thought in this case to be associated with carbapenem resistance, imposes a health risk if disseminated, limiting therapeutic options.

Improving the effectiveness of voice search systems through partial query modification

AbstractThis paper addresses the importance of improving the effectiveness of voice search systems through partial query modification. A user‐centered experiment was designed to compare the effectiveness of an experimental system using partial query modification feature to a baseline system in which users could issue complete queries only, with 32 participants each searching on eight different tasks. The results indicate that the participants spent significantly more time preparing the modification but significantly less time speaking the modification by using the experimental system than by using the baseline system. The participants found that the experimental system (a) was more effective, (b) gave them more control, (c) was easier for the search tasks, and (d) saved them time than the baseline system. The results contribute to improving future voice search system design and benefiting the research community in general. System implications and future work were discussed.

Clinical, Radiographic, and Histopathologic Evaluation of Multiple, Non-Syndromic Hyperplastic Dental Follicles: A Unique Pediatric Case Report

Objectives: Physical and mental disabilities, the existence of financial restrictions, insufficient familial support, transportation problems, and confined access to dental services can have harmful effects on the oral health and overall quality of life of the elderly. Moreover, the incidence of the Covid-19 epidemic has created an additional concern in providing appropriate dental care. Therefore, in this article, geriatric dental education and management due to the Covid-19 pandemic in both developing and developed countries are precisely discussed and compared. Methods: A complete query was carried out on PubMed, PubMed Central, Medline, EBSCO, Google Scholar, and Embase databases, and the studies published during 2010-2022 were collected using the keywords "Geriatric Dentistry," "Covid-19," "Education," "Artificial Intelligence," and "Mobile Dentistry." Ultimately, 41 relevant articles and 2 textbooks were selected and evaluated. Results: The existing crisis can be properly managed by preparing an accurate curriculum emphasizing practical preventive care and effective communication skills, holding domestic or international conferences for dental students, organizing mandatory practical dental courses in nursing homes, and evolving new theories including artificial intelligence and mobile dentistry. Conclusion: It is essential to have a multidisciplinary approach including dentistry, medicine, education, engineering, and legislation to achieve the best treatment plan for the elderly.

Geriatric Dental Management and Education Due to the Covid-19 Crisis; Challenges and Solutions

Objectives: Physical and mental disabilities, the existence of financial restrictions, insufficient familial support, transportation problems, and confined access to dental services can have harmful effects on the oral health and overall quality of life of the elderly. Moreover, the incidence of the Covid-19 epidemic has created an additional concern in providing appropriate dental care. Therefore, in this article, geriatric dental education and management due to the Covid-19 pandemic in both developing and developed countries are precisely discussed and compared. Methods: A complete query was carried out on PubMed, PubMed Central, Medline, EBSCO, Google Scholar, and Embase databases, and the studies published during 2010-2022 were collected using the keywords "Geriatric Dentistry," "Covid-19," "Education," "Artificial Intelligence," and "Mobile Dentistry." Ultimately, 41 relevant articles and 2 textbooks were selected and evaluated. Results: The existing crisis can be properly managed by preparing an accurate curriculum emphasizing practical preventive care and effective communication skills, holding domestic or international conferences for dental students, organizing mandatory practical dental courses in nursing homes, and evolving new theories including artificial intelligence and mobile dentistry. Conclusion: It is essential to have a multidisciplinary approach including dentistry, medicine, education, engineering, and legislation to achieve the best treatment plan for the elderly

SamQL: a structured query language and filtering tool for the SAM/BAM file format

BackgroundThe Sequence Alignment/Map Format Specification (SAM) is one of the most widely adopted file formats in bioinformatics and many researchers use it daily. Several tools, including most high-throughput sequencing read aligners, use it as their primary output and many more tools have been developed to process it. However, despite its flexibility, SAM encoded files can often be difficult to query and understand even for experienced bioinformaticians. As genomic data are rapidly growing, structured, and efficient queries on data that are encoded in SAM/BAM files are becoming increasingly important. Existing tools are very limited in their query capabilities or are not efficient. Critically, new tools that address these shortcomings, should not be able to support existing large datasets but should also do so without requiring massive data transformations and file infrastructure reorganizations.ResultsHere we introduce SamQL, an SQL-like query language for the SAM format with intuitive syntax that supports complex and efficient queries on top of SAM/BAM files and that can replace commonly used Bash one-liners employed by many bioinformaticians. SamQL has high expressive power with no upper limit on query size and when parallelized, outperforms other substantially less expressive software.ConclusionsSamQL is a complete query language that we envision as a step to a structured database engine for genomics. SamQL is written in Go, and is freely available as standalone program and as an open-source library under an MIT license, https://github.com/maragkakislab/samql/.

SkinnerDB: Regret-bounded Query Evaluation via Reinforcement Learning

SkinnerDB uses reinforcement learning for reliable join ordering, exploiting an adaptive processing engine with specialized join algorithms and data structures. It maintains no data statistics and uses no cost or cardinality models. Also, it uses no training workloads nor does it try to link the current query to seemingly similar queries in the past. Instead, it uses reinforcement learning to learn optimal join orders from scratch during the execution of the current query. To that purpose, it divides the execution of a query into many small time slices. Different join orders are tried in different time slices. SkinnerDB merges result tuples generated according to different join orders until a complete query result is obtained. By measuring execution progress per time slice, it identifies promising join orders as execution proceeds. Along with SkinnerDB, we introduce a new quality criterion for query execution strategies. We upper-bound expected execution cost regret, i.e., the expected amount of execution cost wasted due to sub-optimal join order choices. SkinnerDB features multiple execution strategies that are optimized for that criterion. Some of them can be executed on top of existing database systems. For maximal performance, we introduce a customized execution engine, facilitating fast join order switching via specialized multi-way join algorithms and tuple representations. We experimentally compare SkinnerDB’s performance against various baselines, including MonetDB, Postgres, and adaptive processing methods. We consider various benchmarks, including the join order benchmark, TPC-H, and JCC-H, as well as benchmark variants with user-defined functions. Overall, the overheads of reliable join ordering are negligible compared to the performance impact of the occasional, catastrophic join order choice.

Word-embedding-based query expansion: Incorporating Deep Averaging Networks in Arabic document retrieval

One of the main issues associated with search engines is the query–document vocabulary mismatch problem, a long-standing problem in Information Retrieval (IR). This problem occurs when a user query does not match the content of stored documents, and it affects most search tasks. Automatic query expansion (AQE) is one of the most common approaches used to address this problem. Various AQE techniques have been proposed; these mainly involve finding synonyms or related words for the query terms. Word embedding (WE) is one of the methods that are currently receiving significant attention. Most of the existing AQE techniques focus on expanding the individual query terms rather the entire query during the expansion process, and this can lead to query drift if poor expansion terms are selected. In this article, we introduce Deep Averaging Networks (DANs), an architecture that feeds the average of the WE vectors produced by the Word2Vec toolkit for the terms in a query through several linear neural network layers. This average vector is assumed to represent the meaning of the query as a whole and can be used to find expansion terms that are relevant to the complete query. We explore the potential of DANs for AQE in Arabic document retrieval. We experiment with using DANs for AQE in the classic probabilistic BM25 model as well as for two recent expansion strategies: Embedding-Based Query Expansion approach (EQE1) and Prospect-Guided Query Expansion Strategy (V2Q). Although DANs did not improve all outcomes when used in the BM25 model, it outperformed all baselines when incorporated into the EQE1 and V2Q expansion strategies.

PoBery: Possibly-complete Big Data Queries with Probabilistic Data Placement and Scanning

In big data query processing, there is a trade-off between query accuracy and query efficiency, for example, sampling query approaches trade-off query completeness for efficiency. In this article, we argue that query performance can be significantly improved by slightly losing the possibility of query completeness, that is, the chance that a query is complete. To quantify the possibility, we define a new concept, Probability of query Completeness (hereinafter referred to as PC). For example, If a query is executed 100 times, PC = 0.95 guarantees that there are no more than 5 incomplete results among 100 results. Leveraging the probabilistic data placement and scanning, we trade off PC for query performance. In the article, we propose PoBery (POssibly-complete Big data quERY), a method that supports neither complete queries nor incomplete queries, but possibly-complete queries. The experimental results conducted on HiBench prove that PoBery can significantly accelerate queries while ensuring the PC. Specifically, it is guaranteed that the percentage of complete queries is larger than the given PC confidence. Through comparison with state-of-the-art key-value stores, we show that while Drill-based PoBery performs as fast as Drill on complete queries, it is 1.7 ×, 1.1 ×, and 1.5 × faster on average than Drill, Impala, and Hive, respectively, on possibly-complete queries.

Semantic-Based and Entity-Resolution Fusion to Enhance Quality of Big RDF Data

Within an organisation, the quality in big data is a cornerstone to operational, transactional processes and to the reliability of business analytics for decision making. In fact, as organizations are harnessing multi-sources data to rise the benefits of their business, the quality of data becomes important and crucial. This paper presents a new approach to query big data sources using Resource Description Framework (RDF) representation to ensure data quality by harvesting more relevant and complete query results. Our approach handles two important types of heterogeneity over multiple data sources: semantic heterogeneity and URI-based entity identification. It proposes (1) a semantic entity resolution method based on inference mechanism using rules to manage the misunderstanding of data, in real world entities (2) Data Quality enhancement using MapReduce-based query rewriting approach includes the entity resolution results to infer and adds implicit data into query results (3) a parallel combination of MapReduce jobs of saturation and query rewriting inferences to handle transitive and cyclic rules for a richer rules' expression language (4) experiments to assess the efficiency of the proposed approach over real big RDF data originating from insurance and synthetic data sets.

An efficient and scalable multi-dimensional indexing scheme for modular data centers

An efficient distributed indexing scheme plays an important role in improving the performance of cloud storage systems. To achieve concurrent query service and high manageability, the indexing scheme should meet the requirements of high scalability and low latency. In this paper, we propose RB-Index, an efficient and scalable multi-dimensional indexing scheme for modular data centers with the BCube topology. RB-Index is a two-layer indexing scheme integrating the BCube based routing protocol and the R-tree based indexing structure. In RB-Index, we build several distinct indexing spaces with dimensions selected according to query history. Each server takes responsibility for a portion of the indexing space according to a mapping scheme. A data pretreatment method and a publishing scheme are presented to uniformly distribute the global index across all the servers in the network. Index maintenance strategies are designed to keep the system cost at a low level. Efficient and complete query strategies are also introduced to support highly concurrent queries. We conduct experiments on Amazon EC2 platform to evaluate the performance of RB-Index and compare its performance with RT-CAN and FT-Index. Experiment results manifest the efficiency and scalability of our indexing scheme.

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.

Practical Two-Way QKD-Based Quantum Private Query with Better Performance in User Privacy

Quantum privacy 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-sensitive, 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 two-way QKD-based QPQ protocol with better user privacy protection than cheat-sensitive based on order rearrangement of qubits. The proposed QPQ protocol can resist the Trojan horse attack even without wavelength filter and photon number splitter equipped with auxiliary monitoring detectors.

Examining users' partial query modification patterns in voice search

This article investigates how to improve the effectiveness of voice search systems. Earlier research found that participants employed voice search much less frequently than keyboard search. The main reasons that participants disliked voice search are system mistakes and the inability to modify queries. In keyboard search, query reformulation is facilitated by partial query modification, which is not supported by most of the current voice search systems. Consequently, users need to speak the complete query in voice search even with only minor changes. This article focuses on examining partial query modification during voice search through a Wizard of Oz user experiment. It examines if users would prefer partial query modification and how they perform it in voice search. Thirty‐two participants participated in the experiment. Results indicated that when given the opportunity, the users performed more partial query modifications than complete queries. Common partial query modification strategies and patterns emerged from the experiment. The results can be used to improve the voice search system design and benefit the research community in general. System implications and future work were discussed.

Mining Query Plans for Finding Candidate Queries and Sub-Queries for Materialized Views in BI Systems Without Cube Generation

Materialized views are important for optimizing Business Intelligence (BI) systems when they are designed without data cubes. Selecting candidate queries from large number of queries for materialized views is a challenging task. Most of the work done in the past involves finding out frequent queries from the past workload and creating materialized views from such queries by either manually analyzing workload or using approximate string matching algorithms using query text. Most of the existing methods suggest complete queries but ignore query components such as sub queries for creation of materialized views. This paper presents a novel method to determine on which queries and query components materialized views can be created to optimize aggregate and join queries by mining database of query execution plans which are in the form of binary trees. The proposed algorithm showed significant improvement in terms of more number of optimized queries because it is using the execution plan tree of the query as a basis of selection of query to be optimized using materialized views rather than choosing query text which is used by traditional methods. For selecting a correct set of queries to be optimized using materialized views, the paper proposes efficient specialized frequent tree component mining algorithm with novel heuristics to prune search space. These frequent components are used to determine the possible set of candidate queries for creation of materialized views. Experimentation on standard, real and synthetic data sets, and also the theoretical basis, proved that the proposed method is able to optimize a large number of queries with less number of materialized views and showed a significant improvement in performance compared to traditional methods.

Tractable queries on big data via preprocessing with logarithmic-size output

To provide a dichotomy between those queries that are feasible on big data after appropriate preprocessing and those for which preprocessing does not help, Fan et al. developed the $$\sqcap $$ -tractability theory, which provides a formal foundation on the tractability of query classes in the context of big data. Inspired by some technologies used to deal with big data, we introduce a novel notion of $$\sqcap '$$ -tractability in this paper. We place a restriction on preprocessing functions, which limits the functions to produce relatively short outputs, at most logarithmic-size of the inputs. We set a complexity class to denote the classes of Boolean queries that are $$\sqcap '$$ -tractable and conclude that it is properly contained in that of $$\sqcap $$ -tractable query classes, after discovering that a $$\sqcap $$ -tractable query class is not $$\sqcap '$$ -tractable. With an existing reduction, which does not allow re-factorizing data and query parts, we define complete query classes for the complexity class and give an efficient way to detect such query classes. We also investigate the query classes that can be made $$\sqcap '$$ -tractable and prove that all PTIME classes of Boolean queries can be made $$\sqcap '$$ -tractable.

As-You-Type Social Aware Search

Modern search applications feature real-time as-you-type query search. In its elementary form, the problem consists in retrieving a set of k search results, that is, performing a search with a given prefix, and showing the top-ranked results. In this article, we focus on as-you-type keyword search over social media, that is, data published by users who are interconnected through a social network. We adopt a “network-aware” interpretation for information relevance, by which information produced by users who are closer to the user issuing a request is considered more relevant. This query model raises new challenges for effectiveness and efficiency in online search, even when the intent of the user is fully specified, as a complete query given as input in one keystroke. This is mainly because it requires a joint exploration of the social space and traditional IR indexes, such as inverted lists. We describe a memory-efficient and incremental prefix-based retrieval algorithm, which also exhibits an anytime behavior, allowing output of the most likely answer within any chosen runtime limit. We evaluate our approach through extensive experiments for several applications and search scenarios. We consider searching for posts in microblogging (Twitter and Tumblr), for businesses (Yelp), as well as for movies (Amazon) based on reviews. We also conduct a series of experiments comparing our algorithm with baselines using state-of-the-art techniques and measuring the improvements brought by several key optimizations. They show that our solution is effective in answering real-time as-you-type searches over social media.