Basic Query Research Articles

Can ChatGPT AI Replace or Contribute to Experts’ Diagnosis for Renovation Measures Identification?

Building energy renovations demand expertise from professionals to guide processes, including diagnostics, project planning, interventions, and maintenance. The emergence of open-access AI, like ChatGPT in November 2022, offers new possibilities for improving these processes by assisting or potentially replacing human experts. This study explores the effectiveness of ChatGPT in diagnosing energy renovation measures. Initial assessments involve basic queries to the AI, followed by the inclusion of additional data and secondary questions to gauge its full diagnostic potential. An existing building case from the literature is given to the AI to define the best energy renovation measures. Expert evaluations and comparisons with research-backed solutions assess the AI’s performance using different degrees of questioning details over 60 repetitions. The results indicate that ChatGPT can provide valuable insights and generate comprehensive lists of feasible measures and preliminary cost calculations and payback, but, in general, it lacks depth and quality without specialized input and preparation. A significant quality improvement was found between the tests with 2023 and 2024 AI versions. Open-access AI proves capable of enhancing renovation diagnostics but remains a complement rather than a replacement for building renovation expert judgment. This research underscores the potential of mainstream AI to democratize access to knowledge, albeit with limitations tied to its dependence on quality inputs and contextual expertise.

Method of Collaboration between Humans and Generative Artificial Intelligence in the Development of Information Systems

We propose a method for information system (IS) design with a focus on the applicability of intelligent chatbots. An analysis of existing chatbots shows that current solutions do not have the ability to formulate IS requirements during designing without human intervention. The study proposes to integrate ChatGPT (GPT4 model) as a chatbot to solve the problem of writing requirements elicitation for IS. The proposed method for solving the problem is based on a gradual reduction in entropy of chatbot response, which can be achieved by controlling such basic query parameters as the form, depth and breadth of the prompt. Based on the proposed method, an algorithm for controling prompts during a dialogue with a chatbot was developed, regulating the form, depth and breadth of the question to obtain the required level of content in the answer. The following results were obtained during the study: a method for obtaining new knowledge based on entropy of chatbot response reduction; algorithm for prompts control; the second phase of IS development is automated (including identification of business requirements and system objectives; analysis of existing solutions and technologies; determination of functional requirements and system capabilities; development of system architecture, including service definition and interaction; creation of interfaces for service interaction; establishment of a data model and database and formulation of business process logic). The method was tested to design an IS to support strategic decision making in the forestry industry. The design results received a positive assessment from experts. Analysis of the obtained results revealed the advantages and limitations of using chatbots as co-pilots in the design of IS and outlined directions for future research.

Where the Patterns Are: Repetition-Aware Compression for Colored de Bruijn Graphs.

We describe lossless compressed data structures for the colored de Bruijn graph (or c-dBG). Given a collection of reference sequences, a c-dBG can be essentially regarded as a map from k-mers to their color sets. The color set of a k-mer is the set of all identifiers, or colors, of the references that contain the k-mer. While these maps find countless applications in computational biology (e.g., basic query, reading mapping, abundance estimation, etc.), their memory usage represents a serious challenge for large-scale sequence indexing. Our solutions leverage on the intrinsic repetitiveness of the color sets when indexing large collections of related genomes. Hence, the described algorithms factorize the color sets into patterns that repeat across the entire collection and represent these patterns once instead of redundantly replicating their representation as would happen if the sets were encoded as atomic lists of integers. Experimental results across a range of datasets and query workloads show that these representations substantially improve over the space effectiveness of the best previous solutions (sometimes, even dramatically, yielding indexes that are smaller by an order of magnitude). Despite the space reduction, these indexes only moderately impact the efficiency of the queries compared to the fastest indexes.

Utilizing Artificial Intelligence Application for Diagnosis of Oral Lesions and Assisting Young Oral Histopathologist in Deriving Diagnosis from Provided Features - A Pilot study.

AI in healthcare services is advancing every day, with a focus on uprising cognitive capabilities. Higher cognitive functions in AI entail performing intricate processes like decision-making, problem-solving, perception, and reasoning. This advanced cognition surpasses basic data handling, encompassing skills to grasp ideas, understand and apply information contextually, and derive novel insights from previous experiences and acquired knowledge. ChatGPT, a natural language processing model, exemplifies this evolution by engaging in conversations with humans, furnishing responses to inquiries. We aimed to understand the capability of ChatGPT in solving doubts pertaining to symptoms and histological features related to subject of oral pathology. The study's objective is to evaluate ChatGPT's effectiveness in answering questions pertaining to diagnoses. This cross-sectional study was done using an AI-based ChatGPT application that provides free service for research and learning purposes. The current version of ChatGPT3.5 was used to obtain responses for a total of 25 queries. These randomly asked questions were based on basic queries from patient aspect and early oral histopathologists. These responses were obtained and stored for further processing. The responses were evaluated by five experienced pathologists on a four point liekart scale. The score were further subjected for deducing kappa values for reliability. A total of 25 queries were solved by the program in the shortest possible time for an answer. The sensitivity and specificity of the methods and the responses were represented using frequency and percentages. Both the responses were analysed and were statistically significant based on the measurement of kappa values. The proficiency of ChatGPT in handling intricate reasoning queries within pathology demonstrated a noteworthy level of relational accuracy. Consequently, its text output created coherent links between elements, producing meaningful responses. This suggests that scholars or students can rely on this program to address reasoning-based inquiries. Nevertheless, considering the continual advancements in the program's development, further research is essential to determine its accuracy levels in future versions.

Developing Business Process and Query Skills for Solving Business Problems

ABSTRACT This case offers learning experiences for (1) following a business process diagram for car dealers’ recording of sales and sales returns of new cars and the car manufacturer’s subsequent recording of the same transactions and (2) querying a database to determine why dashboards maintained by the dealers and the manufacturer do not always agree. Scaffolding resources (videos and script with screenshots) enable learners with no querying experience to work the case as they develop basic query skills. For learners with query skills, the case (without scaffolding resources) can be used as a data analysis project covering all major query operations. The data can be queried in any structured query language (SQL) platform. The case offers practice and quiz questions for assessing business process skill and the query skills of joining tables, aggregating data, setting criteria, using built-in functions, developing expressions, and verifying query correctness.

Updated statistics on Influenza mortality.

We have planned this analysis to provide current statistics on mortality directly caused by Influenza viruses in recent years in the US. We performed an electronic search in the online database CDC WONDER to obtain current statistics on direct mortality caused by Influenza viruses in the US. Mortality data are derived from information on all death certificates issued in the 50 states and the District of Columbia, excluding deaths of nonresidents. Our basic query criteria included Influenza-specific ICD-10 codes. Influenza caused an average of 7,670 deaths peryear from 2018 to 2020 based on Influenza-specific ICD-10 codes, with a corresponding mean death rate of 2.3×100,000. The death rate increased in parallel with theage of the US resident population, from 0.2×100,000 in the 5-24 age group to 37.4×100,000 in US residents aged 85 years or older. No substantial differences were observed in males vs. females. The results of this analysis show that Influenza remains a significant clinical burden in the general population, with a cumulative mortality rate of approximately 2.3×100,000, but increasing more than tenfold (to over 37×100,000) in older persons.

Health Care Chatbot

Artificial intelligence (AI) is at the forefront of transforming numerous aspects of our lives by modifying the way we analyze information and improving decision-making through problem solving, reasoning, and learning. Machine learning (ML) is a subset of AI that improves its performance based on the data provided to a generic algorithm from experience rather than defining rules in traditional approaches. Advancements in ML have provided benefits in terms of accuracy, decision-making, quick processing, cost-effectiveness, and handling of complex data. Chatbots, also known as chatter robots, smart bots, conversational agents, digital assistants, or intellectual agents, are prime examples of AI systems that have evolved from ML. This paper presents Healthcare Chatbot using Artificial Intelligence that can make a human-system interaction to resolve basic queries regarding health parameters before consulting a doctor. The actual purpose behind this work is to work on the user's symptoms and to provide medical suggestions according to it, to reduce the time and cost required for the process. The chatbot works on provided input by the user, It takes sentence keywords and makes decisions to solve the user's query and answers it accordingly. Given these effectual benefits, it is not surprising that chatbots have rapidly evolved over the past 2 decades and integrated themselves into numerous fields, such as entertainment, travel, gaming, robotics, and security. Chatbots have been proven to be particularly applicable in various health care components that usually involve face-to-face interactions. With their ability for complex dialog management and conversational flexibility, integration of chatbot technology into clinical practice may reduce costs, refine workflow efficiencies, and improve patient outcomes. A user can be able to recognize the actual disease by providing symptoms of it. As if a person will also know about the solutions or we can say precautions and remedies that they should take accordingly.

Privacy-Preserving Shortest Distance Query for Dynamic Large-Scale Road Network

The road network plays an important role in guiding people's daily travel. In the road network, the shortest distance query is one of the most basic query operations. With the scale of the road network continuously expanding, people choose to outsource the road network to the cloud server in the form of an encrypted graph. To the best of authors' knowledge, the existing privacy-preserving shortest distance query schemes over large-scale encrypted graphs are all designed for the static graph. Nonetheless, the graph of road network is often updated over time. These static privacy-preserving shortest distance query schemes cannot work well in this practical application. In order to address this problem, we explore how to implement the privacy-preserving shortest distance query in an incremental large-scale road network. We use the 2-hop cover label index as the basic secure index structure, which can realize the shortest distance fast query. In order to update the 2-hop cover label index safely and efficiently, we combine a special adjacency index as an auxiliary index structure with homomorphic encryption to implement a dynamic incremental update algorithm for the encrypted graph. We prove that the proposed scheme achieves adaptive semantic security and verify its practicality through experiments.

Method of generating ORM models software code based on relational database schemes

Abstract. Problem. Databases and data stores are an important part of any system, regardless of the field of application. It is at this level that all system information is stored, such as a list of online store products, user personal data, system configuration parameters, etc. In addition, they allow to structure information in the desired format. Although the use of databases is associated with obvious advantages, it has some disadvantages, in particular the process of designing them and integrating their software code is labor-intensive and time-consuming. This problem is usually attempted to be circumvented by using ORM libraries, which provide a convenient programming interface for performing basic queries. But the process of setting up an ORM library including developing ORM models still takes a lot of time. Goal. The purpose of the work is to develop an algorithm for the automatic generation of software code for ORM models based on the selected ORM library and the data scheme presented in the form of a configuration file. To achieve the goal, it is necessary to establish the optimal representation of the data scheme in text format, to determine the structure of the data configuration scheme and to describe all stages of the generation of software code for ORM models. Methodology. System approach, analysis of existing methods of visual design, formats of text representation of configuration data and methods of configuration data storing. Results. The developed method for software code generation for the selected ORM library based on the scheme of the relational database provides the user with the opportunity to obtain a qualitatively designed software code with minimal time expenditure. Originality. The developed method implements a unique approach which allows to create program code for various ORM libraries, supports the syntax and features of a specific programming language, and provides work with data schemas from various relational databases. Practical value. Developed generation algorithm can be used as part of the software in which the automated process of preparing the data schema in the appropriate text formatted form will be implemented. As a result, such a web-application will represent a platform for working with the data layer for IT professionals.

A Cost-Driven Top-K Queries Optimization Approach on Federated RDF Systems

RDF (Resource Description Framework) is a model widely used to construct knowledge bases, while SPARQL (SPARQL Protocol and RDF Query Language) is the standardized structured query language to manipulate RDF data. Recently, many data providers have published their RDF datasets in their own autonomous sites and provided SPARQL query interfaces, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">RDF sources</i> . In order to integrate multiple RDF sources, researchers put forward the federated RDF system to support the federated SPARQL queries. However, existing studies can only support efficient basic queries but not top-k queries. Toward this end, we propose a cost-driven top-k queries optimization approach in federated RDF systems, which can support both top-k queries for single variable ordering and expression ordering. Firstly, we propose an optimized query decomposition method to decompose the federated query into multiple subqueries. Secondly, while considering the top-k operator, we propose a cost model to evaluate the query cost and join cost of subqueries. The optimal query plan can be obtained by the costed-based query plan generation algorithm. Finally, combined with the characteristics of top-k queries, an incremental query plan execution strategy is developed to minimize the total query cost. Experimental results show that the proposed method is effective, efficient and scalable.

Nominal Frictions and Optimal Monetary Policy

It is well known in standard economic literature that nominal frictions have significant impact on the transmission mechanism of monetary policy. This paper considers a closed economy version of DSGE model with various nominal frictions vis-à-vis monetary-cumfiscal blocks to seek the basic query that how monetary policy impacts while in the presence of nominal frictions, like price stickiness, staggered wages, etc. Using Bayesian Simulation techniques, we estimate the model for the closed economy. Our simulation results show that despite the apparent similarities of various frictions, their responses to shocks and fit to data are quite different and there is no agreement on their relative performance. As a result, Monetary Authorities cannot afford to rely on a single reference model which contains few nominal frictions of the economy but need to model a large number of alternative ways available when they take their decision of optimal monetary policy. JEL classification: E32, E37 Keywords: DSGE Models, Nominal Trictions, Monetary Policy

A Twig-Based Algorithm for Top-k Subgraph Matching in Large-Scale Graph Data

Subgraph matching aims to find similar substructures in a single graph according to a given query graph and is known as a basic query for graph data management. There exist many categories of subgraph matching solutions. Subgraph isomorphism, which is thought of an NP-complete problem, is an initial solution for the subgraph matching task. To speed up the procedure, graph simulation has been presented to match subgraphs with a polynomial complexity of time. Unfortunately, graph simulation usually loses topologies of matched subgraphs because of its loose restrictions. In this paper, we propose an approximation approach named kSGM (top- k S ubraph G raph M atching) for subgraph matching based on twig patterns. First, we transform query graphs into twig patterns and match candidate substructures in graph data. Second, we present an optimized join strategy along with top-k mechanism, including join order selection based on cost evaluation and optimized pruning based on maximum/minimum possible score. Finally, we design experiments on real-life and synthetic graph data to evaluate the performance of our work. The results show that our proposed kSGM obviously reduces the time complexity and guarantee the correctness for answering the queries of subgraph matching compared to existing algorithms.

FPIRPQ: Accelerating regular path queries on knowledge graphs

With the growing popularity and application of knowledge-based artificial intelligence, the scale of knowledge graph data is dramatically increasing. As an essential type of query for RDF graphs, Regular Path Queries (RPQs) have attracted increasing research efforts, which explore RDF graphs in a navigational manner. Moreover, path indexes have proven successful for semi-structured data management. However, few techniques can be used effectively in practice for processing RPQ on large-scale knowledge graphs. In this paper, we propose a novel indexing solution named FPIRPQ (Frequent Path Index for Regular Path Queries) by leveraging Frequent Path Mining (FPM). Unlike the existing approaches to RPQs processing, FPIRPQ takes advantage of frequent paths, which are statistically derived from the data to accelerate RPQs. Furthermore, since there is no explicit benchmark targeted for RPQs over RDF graph yet, we design a micro-benchmark including 12 basic queries over synthetic and real-world datasets. The experimental results illustrate that FPIRPQ improves the query efficiency by up to orders of magnitude compared to the state-of-the-art RDF storage engine.

Construction of 3D Design Model of Urban Public Space Based on ArcGIS Water System Terrain Visualization Data

On the premise of being familiar with ArcGIS Server technology, we build the architecture of the entire platform, including the basic support layer, data layer, service platform layer, and application layer, and build the entire environment of the platform. We make electronic maps through Arc Map, and collect, organize, and improve spatial data and attribute data, so as to achieve satisfactory accuracy and visual comfort. This study implements various map services under the Dojo framework, including basic map operations, information display and query, marker points, eagle eye diagrams, measurement, printing, and other functions, and uses JavaScript technology to improve user experience. We publish various services through ArcGIS Server, and realize fast and error-free invocation of each service. Based on the theory of runoff and runoff, ArcGIS software was used to study the hydrological information of the watershed, and to determine the catchment area threshold and hydrological response unit. Combined with the GIS spatial analysis method, the numerical simulation of rainfall and runoff in the study case area was carried out, and the variation of the annual rainfall-runoff coefficient was obtained. This study selects an area where stock planning was first proposed as the object of this research. Briefly, we introduce the construction of three-dimensional public space in a certain area, select thirteen typical three-dimensional public spaces as representatives for public evaluation, and explore their existing problems, mainly including the lack of adaptability of space functions and the lack of diversity in space design, privatization of operation management, low level of public perception, etc. Since then, in response to the public problems of the three-dimensional public space in a certain area, a targeted three-dimensional public space optimization strategy is proposed from the four levels of planning policy, urban design, management subject, and user subject.

FPGA-Accelerated Searchable Encrypted Database Management Systems for Cloud Services

The use of database management systems (DBMSs) as a cloud service is rapidly expanding. Cloud DBMSs offer many advantages, such as easier management, lower costs, and greater scalability. However, there are still security concerns regarding attacks from adversaries. DBMSs that use searchable encryption have been investigated with regard to ensuring their security. Because searchable encryption allows query execution over encrypted data in the cloud, sensitive data can be securely stored there in the cloud. On the other hand, encrypted query processing is slower than query processing on plaintext data. In this article, we use a field-programmable gate array (FPGA) to accelerate query processing in a searchable encrypted DBMS. We also propose a new cache function to shorten the access time to database tables in a DBMS. According to an evaluation using basic queries, the proposed system has achieved up to 110.7 times speed-up compared with the central processing unit (CPU) processing of a single core. In addition, the proposed system can process queries faster than the plaintext processing on a CPU when processing large amounts of data.

API как способ взаимодействия с социальными сетями

In the realities of the pandemic, chat bots have become indispensable helpers. They do not need a lot of resources and constant human control. A method of interacting with social networks through a specialized software interface Web API, which is the basis of the REST architecture, is considered. The basic structure of requests for receiving and sending data to servers is presented. On the example of the implementation of a chatbot for vk.com, capable of automating a dialogue with users, the main design stages are presented, including the requirements for the implementation and operation mode based on the client-server architecture, implementation and testing. The project server is implemented on a Raspberry Pi4 single-board computer. Demonstrated code for performing basic queries and implemented a Long Polling approach to continuously track and distribute user messages. Methods were formed to obtain the necessary resources from the server, to declare a new resource on the server, to update information on the server, and to delete certain objects from the database. The result was a patented software product "Intelligent assistant of VSUIT for social networks".

Visual Coding of Intents for Safety of Substation Design

The automatic safety review of substation design model greatly depends on the formal representation of the safety constraints residing in the building codes written in natural language. In order to overcome the difficulties arising from the hard coding and semantic web approaches, the visual coding method has been studied for representing safety constraints. Nevertheless, very little research explores the formal representation of safety constraints from the query intent viewpoint. Therefore, seven basic query intents are categorized, and simultaneously their mapping with Gremlin query segments is also defined. Then, the visually coding framework is also developed to simplify the representation of query intents. The case study implies that the visual coding of query intents provides an effective and explicit vehicle to describe the safety constraints. In this way, the natural language safety constraints can be automatically converted into Gremlin codes, and moreover the declarative manner of integrating Gremlin queries not only further save time on manually sequencing multiple query tasks, but also can optimize the traversal of graph database.

DDInter: an online drug-drug interaction database towards improving clinical decision-making and patient safety.

Drug-drug interaction (DDI) can trigger many adverse effects in patients and has emerged as a threat to medicine and public health. Despite the continuous information accumulation of clinically significant DDIs, there are few open-access knowledge systems dedicated to the curation of DDI associations. To facilitate the clinicians to screen for dangerous drug combinations and improve health systems, we present DDInter, a curated DDI database with comprehensive data, practical medication guidance, intuitive function interface, and powerful visualization to the scientific community. Currently, DDInter contains about 0.24M DDI associations connecting 1833 approved drugs (1972 entities). Each drug is annotated with basic chemical and pharmacological information and its interaction network. For DDI associations, abundant and professional annotations are provided, including severity, mechanism description, strategies for managing potential side effects, alternative medications, etc. The drug entities and interaction entities are efficiently cross-linked. In addition to basic query and browsing, the prescription checking function is developed to facilitate clinicians to decide whether drugs combinations can be used safely. It can also be used for informatics-based DDI investigation and evaluation of other prediction frameworks. We hope that DDInter will prove useful in improving clinical decision-making and patient safety. DDInter is freely available, without registration, at http://ddinter.scbdd.com/.

QB4MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web

The accessibility of devices that track the positions of moving objects has attracted many researchers in Mobility Online Analytical Processing (Mobility OLAP). Mobility OLAP makes use of trajectory data warehousing techniques, which typically include a path of moving objects at a particular point in time. The Semantic Web (SW) users have published a large number of moving object datasets that include spatial and non-spatial data. These data are available as open data and require advanced analysis to aid in decision making. However, current SW technologies support advanced analysis only for multidimensional data warehouses and Online Analytical Processing (OLAP) over static spatial and non-spatial SW data. The existing technology does not support the modeling of moving object facts, the creation of basic mobility analytical queries, or the definition of fundamental operators and functions for moving object types. This article introduces the QB4MobOLAP vocabulary, which enables the analysis of mobility data stored in RDF cubes. This article defines Mobility OLAP operators and SPARQL user-defined functions. As a result, QB4MobOLAP vocabulary and the Mobility OLAP operators are evaluated by applying them to a practical use case of transportation analysis involving 8826 triples consisting of approximately 7000 fact triples. Each triple contains nearly 1000 temporal data points (equivalent to 7 million records in conventional databases). The execution of six pertinent spatiotemporal analytics query samples results in a practical, simple model with expressive performance for the enabling of executive decisions on transportation analysis.

An adaptive spark-based framework for querying large-scale NoSQL and relational databases.

The growing popularity of big data analysis and cloud computing has created new big data management standards. Sometimes, programmers may interact with a number of heterogeneous data stores depending on the information they are responsible for: SQL and NoSQL data stores. Interacting with heterogeneous data models via numerous APIs and query languages imposes challenging tasks on multi-data processing developers. Indeed, complex queries concerning homogenous data structures cannot currently be performed in a declarative manner when found in single data storage applications and therefore require additional development efforts. Many models were presented in order to address complex queries Via multistore applications. Some of these models implemented a complex unified and fast model, while others’ efficiency is not good enough to solve this type of complex database queries. This paper provides an automated, fast and easy unified architecture to solve simple and complex SQL and NoSQL queries over heterogeneous data stores (CQNS). This proposed framework can be used in cloud environments or for any big data application to automatically help developers to manage basic and complicated database queries. CQNS consists of three layers: matching selector layer, processing layer, and query execution layer. The matching selector layer is the heart of this architecture in which five of the user queries are examined if they are matched with another five queries stored in a single engine stored in the architecture library. This is achieved through a proposed algorithm that directs the query to the right SQL or NoSQL database engine. Furthermore, CQNS deal with many NoSQL Databases like MongoDB, Cassandra, Riak, CouchDB, and NOE4J databases. This paper presents a spark framework that can handle both SQL and NoSQL Databases. Four scenarios’ benchmarks datasets are used to evaluate the proposed CQNS for querying different NoSQL Databases in terms of optimization process performance and query execution time. The results show that, the CQNS achieves best latency and throughput in less time among the compared systems.