Semantic and pragmatic processing in FIDO: A flexible interface for data-base operations

The subject of the study is the methods of intellectual analysis, namely the construction of a decision tree, associative analysis, the identification of patterns between related events based on data presented by a relational model. The purpose of the study is to analyze the features of information units and data structures, using the example of relational systems that affect the technology of knowledge extraction. Tasks: the article solves the following tasks: to consider the relational data model as the most popular and effective data structure used in intelligent information systems for data processing and storage; to analyze the operations of relational algebra, the operational component of the relational data model regarding the application of aggregate functions; to develop a general formal statement of the problem of knowledge extraction from a relational database; to consider the concept of functional associative rules; the ID3 decision tree generation algorithm focused on data processing in relational systems is analyzed. The following methods are implemented: modern view and trends in the field of data mining; features of building information systems based on relational databases, relational algebra, theory of normalization of relations; analysis of literature on the topic of research; comparative analysis. Results achieved: the relational data model is considered as the most effective data structure used in intelligent information systems for data processing and storage. A group of aggregate functions of relational databases is identified and analyzed with respect to key attributes of the relation, which makes it possible to build logical dependencies between information units of the subject area being analyzed. The task of extracting knowledge from the database is formally formulated. The concept of functional associative rules is introduced. The ID3 decision tree generation algorithm focused on data processing in relational systems is carefully analyzed. The semantic network (SN), built on the basis of the proposed approach, allows to increase the efficiency of decision support systems. Conclusions: the universal approach proposed in the article to build a relational data model of an information system for searching for associative patterns in data allows to solve a whole class of typical tasks in which objects are connected by a "many-to-many" relationship or M →N. The relational database model is proposed as a universal information structure for solving associative analysis tasks and presenting knowledge in the form of a semantic network. The examples given in the article confirm the effectiveness of the developed and considered approaches to solving the problem of data mining in the environment of relational systems. Solving the problem of identifying knowledge in data will allow to improve the quality of management decisions made.

NoSQL and relational databases have been integrated in response to the increasing demand for scalable and efficient data management in hybrid cloud environments. The differences in data structures and query processing methods between these databases present both challenges and opportunities when designing an optimized hybrid system. This study explores the integration of NoSQL and relational databases to maximize data storage, retrieval, and processing efficiency across multiple cloud systems. NoSQL databases excel in handling unstructured and semi-structured data, offering flexibility and scalability, whereas relational databases provide robust consistency and structured query capabilities. Despite the wide availability of relational databases, certain applications require the dynamic adaptability of NoSQL systems, making integration a viable solution. The research evaluates key performance parameters, including query execution speed, scalability, data consistency, and resource utilization. Cloudsim is used for simulation, allowing for an in-depth comparison between standalone and hybrid database models. Experimental results indicate that the proposed hybrid model improves query performance by 30%, reduces latency by 20%, and enhances scalability by 40% compared to using relational or NoSQL databases alone. The novelty of this approach lies in its ability to overcome the limitations of each database type by their strengths in a dynamic cloud environment. The results highlight the effectiveness of hybrid database integration in optimizing cloud-based data management while ensuring seamless operation, adaptability, and resource efficiency.

The main problem has been managing databases because they are continuously expanding quickly and getting more complicated in terms of volume, variety, and velocity. Currently, Relational Database Management Systems (RDMS), also known as SQL, or traditional search engines are primarily used to manage and utilize data collection. Due to their highly semantic properties and applications, relational databases have emerged as powerful and superior during the previous two decades. The handling of a high volume of data and the variability in data type and structure has become a laborious task since Big Data entered the IT industry. Relational databases are inadequate to handle such big data due to their rigid data limitations, structure, relations, and other factors. As a result, data aggregation is rendered impossible. NoSQL databases offer a practical and understandable foundation for combining massive amounts of data, structures, and interactions. Data modeling and migration are now needed in order to define the issue. There isn't yet a tool for switching from relational to no-SQL databases. For this migration, relational (SQL) database queries must be converted to NoSQL database queries. Since relational databases (RDBMS) have found it difficult to keep up with modernization, NoSQL has emerged as the most practical database. This essay aims to highlight and assess the applications of NoSQL, the notion of NoSQL data modeling, and the NoSQL database migration procedure. NoSQL, NoSQL Roadmap, Data Modeling, ETL Approach for Data Migration, Data Loading, and Data Extraction.

Preparing a data set for analysis is generally the most time consuming task in a data mining project, requiring many complex SQL queries, joining tables and aggregating columns. Existing SQL aggregations have limitations to prepare data sets because they return one column per aggregated group. In general, a significant manual effort is required to build data sets, where a horizontal layout is required. We propose simple, yet powerful, methods to generate SQL code to return aggregated columns in a horizontal tabular layout, returning a set of numbers instead of one number per row. This new class of functions is called horizontal aggregations. Horizontal aggregations build data sets with a horizontal denormalized layout (e.g. point-dimension, observation-variable, instance-feature), which is the standard layout required by most data mining algorithms. We propose three fundamental methods to evaluate horizontal aggregations: CASE: Exploiting the programming CASE construct; SPJ: Based on standard relational algebra operators (SPJ queries); PIVOT: Using the PIVOT operator, which is offered by some DBMSs. Experiments with large tables compare the proposed query evaluation methods. Our CASE method has similar speed to the PIVOT operator and it is much faster than the SPJ method. In general, the CASE and PIVOT methods exhibit linear scalability, where as the SPJ method does not. In a relational database, especially with normalized tables, a significant effort is required to prepare a summary data set that can be used as input for a data mining or statistical algorithm. Most algorithms require as input a data set with a horizontal layout, with several Records and one variable or dimension per column. That is the case with models like clustering, classification, regression and PCA; consult. Each research discipline uses different terminology to describe the data set. In data mining the common terms are point-dimension. Statistics literature generally uses observation-variable. Machine learning research uses instance-feature. This article introduces a new class of aggregate functions that can be used to build data sets in a horizontal layout (denormalized with aggregations), automating SQL query writing and extending SQL capabilities. We show evaluating horizontal aggregations is a challenging and interesting problem and we introduced alternative methods and optimizations for their efficient evaluation. II. MOTIVATION As mentioned above, building a suitable data set for data mining purposes is a time- consuming task. This task generally requires writing long SQL statements or customizing SQL Code if it is automatically generated by some tool. There are two main ingredients in such SQL code: joins and aggregations; we focus on the second one. The most widely- known aggregation is the sum of a column over groups of rows. Some other aggregations return the average, maximum, minimum or row count over groups of rows. There exist many aggregations functions and operators in SQL. Unfortunately, all these aggregations have limitations to build data sets for data mining purposes. The main reason is that, in general, data sets that are stored in a relational database (or a data warehouse) come

The crucial role of competent software architecture is essential in managing the challenging of big data processing for both relational and nonrelational databases. Relational databases are designed to structure data for facilitating vertical scalability, while non-relational databases excel in handling vast volumes of unstructured data for enhancing horizontal scalability. Choosing the right database paradigm is determined by the needs of the organization, yet selecting the best option may often be a challenging task. Large number of applications still use relational databases due to its benefits of reliability, flexibility, robustness, and scalability. However, with the rapid growth in web and mobile applications as well as huge amounts of complex unstructured data generated via online and offline platforms, nonrelational databases are compensating for the inefficiency of relational databases. Since selecting the right nonrelational database method for high performing applications from a plethora of possibilities is a challenging task, existing studies are still at emergent stage to compare the performance of different popular nonrelational databases. This paper introduces a novel benchmarking approach for tailoring the comparative study of nonrelational databases. To illustrate our approach, we compare two leading non-relational databases, Aerospike and MongoDB, focusing on their average transaction times to evaluate the database performance. Our comprehensive analysis reveals the strengths of each database in read and write operations for single record and bulk record batch transactions.

The technique of two-mode evaluation described allows function-free ground unit clauses to be stored as relations in a relation data base instead of as statements in a Prolog program and provides a real-time mechanism for retrieving such clauses as necessary when executing the program without programmed data base calls. The technique includes a look-ahead facility for reasonably effective (logical) data base operations. All interactions between the program and the data base are transparent to the programmer, i.e. two-mode evaluation provides a one-memory-level virtual machine from a programming point of view. The formal semantics of an integrated Prolog/RDBMS environment is presented and explained.

A new method is developed to query a relational database in natural language (NL). The method, based on a semantic approach, interprets grammatical and lexical units of a natural language into concepts of subject domain, which are given in a conceptual scheme. The conceptual scheme is mapped formally onto the logical scheme. We applied the method to query the FlyEx database in natural language. FlyEx contains information on the expression of segmentation genes in Drosophila melanogaster. The method allows formulation of queries in various natural languages simultaneously, and is adaptive to changes in the knowledge domain and user's views. It provides optimal transformation of queries from natural language to SQL, as well as visualization of information as a hyperscheme. The method does not require specification of all possible language constructions as well as a standard grammar accuracy in formulation of NL queries.

A relational data base is herein defined as a collection of normalized relations (relations in first normal form) and a collection of domains. A normalized relation may be viewed as a table, wherein each row of the table corresponds to a tuple of the relation, and the entries in a given column belong to the set of values constituting the domain underlying that column. The domains of a data base have an abstract existence apart from the data base relations.The data base also includes various types of semantic integrity rules, which specify additional properties of the data in the data base. One such type of semantic integrity rule is the domain definition. A domain definition includes the precise description of the set of values (objects) constituting the domain. In a normalized data base, all domains are sets of atomic data values. A domain definition also includes a specification of the ordering on the values in a domain, for comparability purposes. In addition, a domain definition contains a specification of the action that is to occur if an attempt is made to violate the restriction that every entry in each column of a relation must be from the underlying domain of that column.A nonprocedural language permitting the high level expression of domain definitions is defined. Language details and examples are presented, and the syntax and informal semantics of the domain definition language are given. This approach to domain definition is analyzed in terms of its impact on other aspects of data base semantic integrity. The relationship with the data base system in general is outlined. An analysis of intradomain and interdomain comparability is included. An introduction to relevant implementation issues is also presented. Emphasis is placed on a general approach to implementation and implementation techniques, rather than on a specific system.

A relational data base is herein defined as a collection of normalized relations (relations in first normal form) and a collection of domains. A normalized relation may be viewed as a table, wherein each row of the table corresponds to a tuple of the relation, and the entries in a given column belong to the set of values constituting the domain underlying that column. The domains of a data base have an abstract existence apart from the data base relations. The data base also includes various types of semantic integrity rules, which specify additional properties of the data in the data base. One such type of semantic integrity rule is the domain definition. A domain definition includes the precise description of the set of values (objects) constituting the domain. In a normalized data base, all domains are sets of atomic data values. A domain definition also includes a specification of the ordering on the values in a domain, for comparability purposes. In addition, a domain definition contains a specification of the action that is to occur if an attempt is made to violate the restriction that every entry in each column of a relation must be from the underlying domain of that column. A nonprocedural language permitting the high level expression of domain definitions is defined. Language details and examples are presented, and the syntax and informal semantics of the domain definition language are given. This approach to domain definition is analyzed in terms of its impact on other aspects of data base semantic integrity. The relationship with the data base system in general is outlined. An analysis of intradomain and interdomain comparability is included. An introduction to relevant implementation issues is also presented. Emphasis is placed on a general approach to implementation and implementation techniques, rather than on a specific system.

A relational data base is herein defined as a collection of normalized relations (relations in first normal form) and a collection of domains. A normalized relation may be viewed as a table, wherein each row of the table corresponds to a tuple of the relation, and the entries in a given column belong to the set of values constituting the domain underlying that column. The domains of a data base have an abstract existence apart from the data base relations. The data base also includes various types of semantic integrity rules, which specify additional properties of the data in the data base. One such type of semantic integrity rule is the domain definition. A domain definition includes the precise description of the set of values (objects) constituting the domain. In a normalized data base, all domains are sets of atomic data values. A domain definition also includes a specification of the ordering on the values in a domain, for comparability purposes. In addition, a domain definition contains a specification of the action that is to occur if an attempt is made to violate the restriction that every entry in each column of a relation must be from the underlying domain of that column. A nonprocedural language permitting the high level expression of domain definitions is defined. Language details and examples are presented, and the syntax and informal semantics of the domain definition language are given. This approach to domain definition is analyzed in terms of its impact on other aspects of data base semantic integrity. The relationship with the data base system in general is outlined. An analysis of intradomain and interdomain comparability is included. An introduction to relevant implementation issues is also presented. Emphasis is placed on a general approach to implementation and implementation techniques, rather than on a specific system.

The XML support in relational databases and the SQL/XML language are still relatively new as compared to purely relational databases and traditional SQL. Today, most database users have a strong relational and SQL background. SQL/XML enables users to perform queries and updates across XML and relational data, but many struggle with writing SQL/XML statements or XQuery update expressions. One reason is the novelty of SQL/XML and of the XQuery expressions that must be included. Another problem is that the tree structure of the XML data may be unknown or difficult to understand for the user. Evolving XML Schemas as well as hybrid XML/relational schemas make it even harder to write SQL/XML statements. Also, legacy applications use SQL but may require access to XML data without costly code changes. Motivated by these challenges, we developed a method to generate SQL/XML query and update statements automatically. The input is either a GUI or a regular SQL statement that uses logical data item names irrespective of their actual location in relational or XML columns in the database. The output is a SQL/XML statement that queries or updates relational and XML data as needed to carry out the original user statement. This relieves the user and simplifies schema evolution and integration. We have prototyped and tested the proposed method on top of DB2 9.5.

A database is information collection that is organized in tables so that it can easily be accessed, managed, and updated. It is the collection of tables, schemas, queries , reports, views and other objects. The data are typically organized to model in a way that supports processes requiring information, such as modelling to find a hotel with availability of rooms, thus the people can easily locate the hotels with vacancies . There are many databases commonly, relational and non relational databases. Relational databases usually work with structured data and non relational databases are work with semi structured data. In this paper, the performance evaluation of MySQL and MongoDB is performed where MySQL is an example of relational database and MongoDB is an example of non relational databases. A relational database is a data structure that allows you to connect information from different 'tables', or different types of data buckets. Non-relational database stores data without explicit and structured mechanisms to link data from different buckets to one another. This paper discuss about the performance of MongoDB and MySQL in the field of Super Market Management System. A supermarket is a large form of the traditional grocery store also a self-service shop offering a wide variety of food and household products, organized in systematic manner. It is larger and has a open selection than a traditional grocery store.

Introduction: Blockchain is a rapidly evolving data security practice which employs a growing list of records linked using cryptographic hash of the block proceeding, a timestamp and transaction data. Electronic medical records (EMR) are gaining popularity in health sector. Most of these EMRs are based on relational databases. Data tampering is a known issue found in relational data base approach and it is fairly simple to alter a relational data base record with the administrative privileges. This research explores the potentials of blockchain technology with its resistant to modification of the data, as a tamper protection mechanism for medical records. Methods: A blockchain-based program was developed using Proof-of-Work, to maintain the integrity of the digital record independent of access permissions. This was preceded by a series of interviews with clinicians and EMR administers to understand the possible data tampering in association with paper-based and electronic medical records in the State health sector of Sri Lanka. The blockchain algorithm was applied to the Hospital Health Information Management System (HHIMS) clinical database. A security audit was used to assess the tamper-protection capabilities of the relational database. Results: It was possible to incorporate the blockchain algorithm to the relational database of Hospital HHIMS successfully. An independent blockchain-based data repository was created in association with the native data base of HHIMS. With this approach, a ‘block’ is being created for each new record added to HHIMS database. Hence, the blockchain generated was instrumental in verifying the authenticity of EMR transactions. Conclusion: With the increasing penetration of EMR in health sector, the integrity of the digital stored clinical data is becoming a vital necessity. In centralized database administration, possibility of tampering is high. Clinical data can be counted as ‘high risk’ for such occurrences. The blockchain technology found to be capable of making relational data base tamper-protected by verifying the authenticity of the transactions and data being stored in HHIMS.

Structured Query Language (SQL) is commonly used in Relational Database Management Systems (RDBMS) and is currently one of the most popular data definition and manipulation languages. Its core functionality is implemented, with only some minor variations, throughout all RDBMS products. It is an effective tool in the process of managing and querying data in relational databases. This paper describes a method to effectively automate the conversion of a data query from a Natural Language Query (NLQ) to Structured Query Language (SQL) with Online Analytical Processing (OLAP) cube data warehouse objects. To obtain or manipulate the data from relational databases, the user must be familiar with SQL and must also write an appropriate and valid SQL statement. However, users who are not familiar with SQL are unable to obtain relevant data through relational databases. To address this, we propose a Natural Language Processing (NLP) model to convert an NLQ into an SQL query. This allows novice users to obtain the required data without having to know any complicated SQL details. The model is also capable of handling complex queries using the OLAP cube technique, which allows data to be pre-calculated and stored in a multi-dimensional and ready-to-use format. A multi-dimensional cube (hypercube) is used to connect with the NLP interface, thereby eliminating long-running data queries and enabling self-service business intelligence. The study demonstrated how the use of hypercube technology helps to increase the system response speed and the ability to process very complex query sentences. The system achieved impressive performance in terms of NLP and the accuracy of generating different query sentences. Using OLAP hypercube technology, the study achieved distinguished results compared to previous studies in terms of the speed of the response of the model to NLQ analysis, the generation of complex SQL statements, and the dynamic display of the results. As a plan for future work, it is recommended to use infinite-dimension (n-D) cubes instead of 4-D cubes to enable ingesting as much data as possible in a single object and to facilitate the execution of query statements that may be too complex in query interfaces running in a data warehouse. The study demonstrated how the use of hypercube technology helps to increase system response speed and process very complex query sentences.

This paper is devoted to present a new approach to natural language understanding which is called here goal-oriented parsing. The interaction in natural language with artificial systems (robots, data base systems, program generators, question-answering systems, etc.) does not require in most cases of actual interest a full (human like) comprehension of natural language in all its details and nuances. A partial understanding is often enough, wich extracts from the natural language expressions the only significant information which is necessary to construct a correct formal input for the target system. In such a model of comprehension the same meaning is assigned to several different natural language expressions, thus defining a many-to-one mapping between natural language sentences and corresponding formal representations. We argue that a bounded scope, restricted, goal-oriented understanding of natural language may greatly increase the efficency of representation models and parsing algorithms, thus allowing the construction of effective systems. This claim is supported by the design and implementation of a natural language interface to a relational data base called NLI and developed at the Milan Polytechnic Artificial Intelligence Project. In the paper the architecture of the system, the linguistic models, and the parsing algorithms are presented and illustrated through selected examples. Promising directions for future research are outlined as well.