Support For Queries Research Articles

The Correlation between Husband Support and Reproductive Age Couple Self-Efficacy in LARC Election in Choosing LARC

Husband support is a strengthening factor in choosing long-term contraceptive methods. The problem is the husband's strong influence in determining the wife’s contraception. A wife becomes more confident in herself through the positive support of her husband. This study aims to find out the correlation between the husband's support with the self-efficacy of women of childbearing age in choosing LARC. The research design used is a correlational descriptive with a cross-sectional approach. The population of LARC acceptor PUS was 42 people, sampling using proportional random sampling with 38 respondents who met the inclusion criteria. The research instrument uses husband support query sheets and self-efficacy questionnaires that have been tested for validity and reliability. The Spearman correlation test shows a correlation coefficient of 0.564 which is strongly correlated. As well as the significance level of 0.000 < 0.05, which means there is a correlation between the husband's support and PUS self-efficacy in choosing LARC. The promotional action to increase the confidence of the use of LARC is to increase the provision of information on LARC contraception.

AN ETHEREUM-BASED ELECTRONIC HEALTH RECORD SYSTEM WITH SQL QUERY SUPPORT

Blockchains’ power of decentralization, immutability, and transparency has found its application in many fields. The blockchain is an append-only structure. As the blockchain grows, accessing blocks from the past in an efficient manner has become a challenging task. Further, blockchains were not initially envisioned to be read-heavy systems. With the passage of time, more and more applications are using blockchains and therefore blockchains need to provide active support for high read loads concerning the history state as well. To facilitate data querying in blockchain, we have proposed an SQL query processing feature in the Ethereum blockchain through a decentralized application. More specifically, we build an Ethereum-based Electronic Health Record (EHR) system with SQL query support. The following approaches for query processing have been explored and implemented: (i) linearly scanning blockchain, (ii) scanning only from a user-specified block, (iii) replication in database, (iv) indexing in a database, and (v) using smart contracts. Our timing analysis across these implementations reveals that the smart contracts-based approach has reasonable performance gains compared the other approaches.

Enhanced calendar support for temporal datacube queries

AbstractA significant share of our data are timeseries. Time can occur as the single dimension, or further (such as spatial) dimensions can be present as well, leading to multidimensional data such as 3D x/y/t satellite image timeseries or 4D x/y/z/t atmospheric simulations like weather forecasts. However, no user would like to count “seconds since epoch,” rather we all prefer calendar notation. Libraries, SQL, etc. support calendar arithmetics, yet more advanced functionality is needed. Instead of demoting advanced calendar functionality to low‐level coding, it seems desirable to have semantic support for calendars at high‐level, declarative query level. We present a conceptual framework for temporal modeling which allows the expression of a wide range of temporal queries. It smoothly enhances existing calendar handling by adding two new data and query description parameters, period of validity, and granularity. While our work is embedded in spatio‐temporal geo services, it can be incorporated in a non‐breaking manner in any environment needing calendar addressing. The concepts are implemented in an Array DBMS operational on several Petabytes of Earth datacubes.

Distance queries over dynamic interval graphs

We design the first dynamic distance oracles for interval graphs, which are intersection graphs of a set of intervals on the real line, and for proper interval graphs, which are intersection graphs of a set of intervals in which no interval is properly contained in another.For proper interval graphs, we design a linear space data structure which supports distance queries (computing the distance between two query vertices) and vertex insertion or deletion in O(lg⁡n) worst-case time, where n is the number of vertices currently in G. Under incremental (insertion only) or decremental (deletion only) settings in general interval graphs, we design linear space data structures that support distance queries in O(lg⁡n) worst-case time and vertex insertion or deletion in O(lg⁡n) amortized time, where n is the maximum number of vertices in the graph. Under fully dynamic settings in general interval graphs, we design a data structure that represents an interval graph G in O(n) words of space to support distance queries in O(nlg⁡n/S(n)) worst-case time and vertex insertion or deletion in O(S(n)+lg⁡n) worst-case time, where n is the number of vertices currently in G and S(n) is an arbitrary function that satisfies S(n)=Ω(1) and S(n)=O(n). This implies an O(n)-word solution with O(nlg⁡n)-time support for both distance queries and updates. All four data structures can answer shortest path queries by reporting the vertices in the shortest path between two query vertices in O(lg⁡n) worst-case time per vertex.We also study the hardness of supporting distance queries under updates over an intersection graph of 3D axis-aligned line segments, which generalizes our problem to 3D. Finally, we solve the problem of computing the diameter of a dynamic connected interval graph.

AeonG: An Efficient Built-in Temporal Support in Graph Databases

Real-world graphs are often dynamic and evolve over time. It is crucial for storing and querying a graph's evolution in graph databases. However, existing works either suffer from high storage overhead or lack efficient temporal query support, or both. In this paper, we propose AeonG, a new graph database with built-in temporal support. AeonG is based on a novel temporal graph model. To fit this model, we design a storage engine and a query engine. Our storage engine is hybrid, with one current storage to manage the most recent versions of graph objects, and another historical storage to manage the previous versions of graph objects. This separation makes the performance degradation of querying the most recent graph object versions as slight as possible. To reduce the historical storage overhead, we propose a novel anchor+delta strategy, in which we periodically create a complete version (namely anchor) of a graph object, and maintain every change (namely delta) between two adjacent anchors of the same object. To boost temporal query processing, we propose an anchor-based version retrieval technique in the query engine to skip unnecessary historical version traversals. Extensive experiments are conducted on both real and synthetic datasets. The results show that AeonG achieves up to 5.73× lower storage consumption and 2.57× lower temporal query latency against state-of-the-art approaches, while introducing only 9.74% performance degradation for supporting temporal features.

Immersive Study Analyzer: Collaborative Immersive Analysis of Recorded Social VR Studies.

Virtual Reality (VR) has become an important tool for conducting behavioral studies in realistic, reproducible environments. In this paper, we present ISA, an Immersive Study Analyzer system designed for the comprehensive analysis of social VR studies. For in-depth analysis of participant behavior, ISA records all user actions, speech, and the contextual environment of social VR studies. A key feature is the ability to review and analyze such immersive recordings collaboratively in VR, through support of behavioral coding and user-defined analysis queries for efficient identification of complex behavior. Respatialization of the recorded audio streams enables analysts to follow study participants' conversations in a natural and intuitive way. To support phases of close and loosely coupled collaboration, ISA allows joint and individual temporal navigation, and provides tools to facilitate collaboration among users at different temporal positions. An expert review confirms that ISA effectively supports collaborative immersive analysis, providing a novel and effective tool for nuanced understanding of user behavior in social VR studies.

Knowledge Graphs – The Future of Integration in CRIS Systems for Uses of Assistance to Scientific Reasoning

Knowledge graphs (KGs) are gaining prominence for their efficacy in data integration and knowledge representation within Current Research Information Systems (CRIS) Systems. By employing a semantic data model to represent entities, attributes, and their relationships, they prove versatile across scientific applications. In the era of Science platformization, particularly within CRIS systems, KGs serve to amalgamate diverse data sources and formats, facilitating the creation of interconnected data models. This enables stakeholders to access comprehensive, consistent information pertinent to their endeavors. This paper examines the pivotal roles of KGs in current and future data integration within CRIS systems, emphasizing their contributions to scientific reasoning. While their benefits include flexible knowledge modeling, support for semantic queries, and interoperability with various data sources, they face systemic limitations, particularly in methodological and technological aspects, hindering classical scientific investigations. The paper underscores the necessity for novel approaches to address these limitations, offering insights, use cases, and best practices for implementing KGs in CRIS systems. This paves the way for research institutions and scientific organizations to enhance their data analytics capabilities and support scientific reasoning effectively.

Integration of Rucio Metadata in Belle II

Rucio is a Data Management software that has become a de-facto standard in the HEP community and beyond. It allows the management of large volumes of data over their full lifecycle. The Belle II experiment located at KEK (Japan) recently moved to Rucio to manage its data over the coming decade (O(10) PB/year). In addition to its Data Management functionalities, Rucio also provides support for storing generic metadata. Rucio metadata already provides accurate accounting of the data stored all over the sites serving Belle II. Annotating files with generic metadata opens up possibilities for finer-grained metadata query support. We will first introduce some of the new developments aimed at providing good performance that were done to cover Belle II use-cases like bulk insert methods, metadata inheritance, etc. We will then describe the various tests performed to validate Rucio generic metadata at Belle II scale (O(100M) files), detailing the import and performance tests that were made.

Enhancing Student Query Support with Dialogflow Based Faq Chatbot

Enhancing Student Query Support with Dialogflow Based Faq Chatbot

Regional Digest Aggregation based on Opportunities in Wireless Sensor Networks

It is desirable to reduce the amount of data collected in sensor networks to reduce energy consumption and to extend network lifetime while one should extract as much information as possible to allow support for heterogeneous user queries. There have been a number of aggregation proposals, aiming at reducing the amount of data communications within the sensor network, mainly focused on supporting limited and simple types of queries such as SUM, COUNT, AVG, MIN/MAX. Unfortunately, user queries are not limited by these simple types of aggregates and cannot be predicted a-priori. In this paper we propose an aggregation-framework that produces regional digests at a parameter defined granularity such that arbitrary user queries can be supported. Since the success of the aggregation policy greatly depends on the integrated routing mechanisms we evaluate the performance of our approach under alternative routing approaches. Our experimental results suggest at least 3-fold improvement in spatial accuracy at a relatively small expense of increased energy consumption.

Report on the 1st Workshop on Task Focused IR in the Era of Generative AI

Search and recommender systems should prioritize support for user tasks over support for individual queries or actions. To that end, the Information Retrieval (IR) community has spent decades trying to understand users' tasks and their contexts, and how to best assist users in making progress toward completing them. The recent advancements in generative artificial intelligence (AI) have drastically shifted the landscape of task-focused IR. Users can now express not only their queries and questions, but actual information needs, tasks, and goals in natural language to an AI system and receive not just results, but also answers in natural language that are generated specifically for them. This new paradigm raises many interesting questions, opportunities, and challenges. We brought together a group of highly motivated students and scholars in a two-day workshop on the Microsoft campus in Redmond to discuss these issues, learn from each other, and envision a new future for task-focused IR and information access more broadly. Date : 28--29 September 2023. Website : https://ir-ai.github.io.

A large scale training sample database system for intelligent interpretation of remote sensing imagery

ABSTRACT Artificial Intelligence (AI) Machine Learning (ML) technologies, particularly Deep Learning (DL), have demonstrated significant potential in the interpretation of Remote Sensing (RS) imagery, covering tasks such as scene classification, object detection, land-cover/land-use classification, change detection, and multi-view stereo reconstruction. Large-scale training samples are essential for ML/DL models to achieve optimal performance. However, the current organization of training samples is ad-hoc and vendor-specific, lacking an integrated approach that can effectively manage training samples from different vendors to meet the demands of various RS AI tasks. This article proposes a solution to address these challenges by designing and implementing LuoJiaSET, a large-scale training sample database system for intelligent interpretation of RS imagery. LuoJiaSET accommodates over five million training samples, providing support for cross-dataset queries and serving as a comprehensive training data store for RS AI model training and calibration. It overcomes challenges related to label semantic categories, structural heterogeneity in label representation, and interoperable data access.

Integrating Logic Rules with Everything Else, Seamlessly

AbstractThis paper presents a language, Alda, that supports all of logic rules, sets, functions, updates, and objects as seamlessly integrated built-ins. The key idea is to support predicates in rules as set-valued variables that can be used and updated in any scope, and support queries using rules as either explicit or implicit automatic calls to an inference function. We have defined a formal semantics of the language, implemented a prototype compiler that builds on an object-oriented language that supports concurrent and distributed programming and on an efficient logic rule system, and successfully used the language and implementation on benchmarks and problems from a wide variety of application domains. We describe the compilation method and results of experimental evaluation.

Self-Adaptive Sampling Based Per-Flow Traffic Measurement

Per-flow traffic measurement in the high-speed network plays an important role in many practical applications. Due to the limited on-chip memory and the mismatch between off-chip memory speed and line rate, sampling-based methods select and forward a part of flow traffic to off-chip memory, which complements sketch-based solutions in estimation accuracy and online query support. However, most current work uses the same sampling probability for all flows, leading to the waste in storage and communication resources. In practice, different flows often require different sampling rates to meet the same accuracy constraint. This paper presents self-adaptive sampling, a framework to sample each flow with a probability adapted to flow size/spread. Then we propose three algorithms, SAS-LC, SAS-LOG, and SAS-HYB. SAS-LC and SAS-LOG are geared towards per-flow spread estimation and per-flow size estimation by using different compression functions. SAS-HYB combines the advantages of SAS-LC and SAS-LOG, showing higher efficiency when both small flows and large flows are interested. We implement our estimators in hardware using NetFPGA. Experimental results based on real Internet traces show that, compared to the state-of-the-art in per-flow spread estimation, SAS-LC can save around 10% on-chip space and reduce up to 40% communication cost for large flows. In per-flow size estimation, SAS-LOG can save 40% on-chip space and reduce up to 96% communication costs for large flows. Moreover, SAS-HYB’s on-chip memory usage will not be larger than SAS-LC or SAS-LOG and can save up to 19% on-chip space than SAS-LOG when both small flows and large flows are interested.

Succinct Data Structures for SP, Block-Cactus and 3-Leaf Power Graphs

We design succinct encodings of series-parallel, block-cactus and 3-leaf power graphs while supporting the basic navigational queries such as degree, adjacency and neighborhood optimally in the RAM model with logarithmic word size. One salient feature of our representation is that it can achieve optimal space even though the exact space lower bound for these graph classes is not known. For these graph classes, we provide succinct data structures with optimal query support for the first time in the literature. For series-parallel multigraphs, our work also extends the works of Uno et al. (Disc. Math. Alg. and Appl., 2013) and Blelloch and Farzan (CPM, 2010) to produce optimal bounds.

Compact representations of spatial hierarchical structures with support for topological queries

Among different spatial data models, the topological model for spatial regions explicitly represents common boundaries. This model pursues the efficiency of topology-related queries and the elimination of data redundancy. This paper proposes several space-efficient data structures to support access to the topological representation of two-dimensional regions that are organized in a multi-granular or hierarchical structure, such as the political and administrative partition of a country. In the context of these hierarchies, we focus on queries that search for inclusion, disjointness, and adjacency between regions. The proposed structures build upon compact planar graph embeddings, which show to have a good trade-off between space and time.

Efficient immediate-access dynamic indexing

In a dynamic retrieval system, documents must be ingested as they arrive, and be immediately findable by queries. Our purpose in this paper is to describe an index structure and processing regime that accommodates that requirement for immediate access, seeking to make the ingestion process as streamlined as possible, while at the same time seeking to make the growing index as small as possible, and seeking to make term-based querying via the index as efficient as possible. We describe a new compression operation and a novel approach to extensible lists which together facilitate that triple goal. In particular, the structure we describe provides incremental document-level indexing using as little as two bytes per posting and only a small amount more for word-level indexing; provides fast document insertion; supports immediate and continuous queryability; provides support for fast conjunctive queries and similarity score-based ranked queries; and facilitates fast conversion of the dynamic index to a “normal” static compressed inverted index structure. Measurement of our new mechanism confirms that in-memory dynamic document-level indexes for collections into the gigabyte range can be constructed at a rate of two gigabytes/minute using a typical server architecture, that multi-term conjunctive Boolean queries can be resolved in just a few milliseconds each on average even while new documents are being concurrently ingested, and that the net memory space required for all of the required data structures amounts to an average of as little as two bytes per stored posting, less than half the space required by the best previous mechanism.

TinyEnc: Enabling Compressed and Encrypted Big Data Stores With Rich Query Support

Encryption and compression are two critical techniques to ensure data confidentiality and efficiency for a cloud-based data storage system, respectively. However, directly combing encryption and compression incurs substantial performance degradation. We propose TinyEnc, an encrypted data storage system for cloud-based key-value store, which supports encryption and compression simultaneously with rich query support. To reconcile encryption and compression without compromising performance, we first propose a new encrypted compression data structure to enable fine-grained access to compressed and encrypted key-value data. We then propose two new transforming mechanisms, namely orthogonal data dividing and hierarchical data padding, to transform a plaintext key-value table into the encrypted compression data structure in a privacy-preserving way. Finally, we craft order-revealing encryption (ORE) and symmetric searchable encryption (SSE) to design a new encrypted search index over the encrypted compression data structure to support rich types of data queries. We implement a prototype of TinyEnc on top of Cassandra. Besides, the evaluation result shows that TinyEnc increases the throughput by up to 7 times and compression ratio by up to 1.3 times with respect to previous works.

Scalability Testing Approach for Internet of Things for Manufacturing SQL and NoSQL Database Latency and Throughput

Abstract The proliferation of low-cost sensors and industrial data solutions has continued to push the frontier of manufacturing technology. Machine learning and other advanced statistical techniques stand to provide tremendous advantages in production capabilities, optimization, monitoring, and efficiency. The tremendous volume of data gathered continues to grow, and the methods for storing the data are critical underpinnings for advancing manufacturing technology. This work aims to investigate the ramifications and design tradeoffs within a decoupled architecture of two prominent database management systems (DBMS): sql and NoSQL. A representative comparison is carried out with Amazon Web Services (AWS) DynamoDB and AWS Aurora MySQL. The technologies and accompanying design constraints are investigated, and a side-by-side comparison is carried out through high-fidelity industrial data simulated load tests using metrics from a major US manufacturer. The results support the use of simulated client load testing for comparing the latency of database management systems as a system scales up from the prototype stage into production. As a result of complex query support, MySQL is favored for higher-order insights, while NoSQL can reduce system latency for known access patterns at the expense of integrated query flexibility. By reviewing this work, a manufacturer can observe that the use of high-fidelity load testing can reveal tradeoffs in IoTfM write/ingestion performance in terms of latency that are not observable through prototype-scale testing of commercially available cloud DB solutions.

An Efficient Index-Based Approach to Distributed Set Reachability on Small-World Graphs

Set reachability query in directed graphs has a plethora of graph-based applications such as dependency analysis and graph centrality calculation. Given two sets <inline-formula><tex-math notation="LaTeX">$S$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$T$</tex-math></inline-formula> of source and target vertices, set reachability query needs to acquire all pairs <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s{\in }S$</tex-math></inline-formula> , <inline-formula><tex-math notation="LaTeX">$t{\in }T$</tex-math></inline-formula> and <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> can reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> . The state-of-the-art approach distributed set reachability (DSR) investigates the set reachability query in a distributed environment and adopts a static graph-based index to enhance the query efficiency. Nevertheless, DSR needs to store the graph-based index in all partitions, which causes a huge space overhead. Furthermore, it cannot efficiently solve the negative query <inline-formula><tex-math notation="LaTeX">$(s,t)$</tex-math></inline-formula> where <inline-formula><tex-math notation="LaTeX">$s$</tex-math></inline-formula> cannot reach <inline-formula><tex-math notation="LaTeX">$t$</tex-math></inline-formula> , since DSR needs to traverse the whole reachable paths and becomes unable to efficiently reduce the computations. To alleviate these issues, we propose a novel multi-level 2-hop (ML2hop) index for the set reachability query in a distributed environment. Based on ML2hop, we further present a bi-directional query algorithm, called MLQA, to achieve efficient support for both positive and negative queries in Pregel-like systems. Generally, MLQA is equipped with the following three significant properties: (1) Low computation costs. It reduces redundant local computations in each partition by controlling the rounds of path traversals. (2) Low communication costs. It restricts the message exchange among different partitions within one single round with guaranteed accuracy of query results. (3) High parallelism. It adopts a bi-directional query technique for message propagation, achieving the better query efficiency than the forward-traversal query strategy utilized in DSR. Experimental results over several real-world graphs demonstrate that MLQA significantly outperforms the state-of-the-art algorithm by up to two orders of magnitude speedup.