Querying connected tuple trees for relational keyword search

This paper compares two approaches to meeting the data handling requirements of Intermedia, a hypermedia system developed at the Institute for Research in Information and Scholarship at Brown University. Intermedia, though written using an object-oriented programming language, relies on a traditional relational database management system for data storage and retrieval. We examine the ramifications of replacing the relational database with an object-oriented database. We begin by describing the important characteristics each database system. We then describe Intermedia and give an overview of its architecture and its data handling requirements. We explain why and how we used a relational database management system and the problems that we encountered with this system. We then present the design of an object-oriented database schema for Intermedia and compare the relational and object-oriented database management system approaches.

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.

Keyword search in relational databases allows the user to search information without knowing database schema and using structural query language. As results needed by user are assembled from connected tuples of multiple relations, ranking keyword queries are needed to retrieve relevant results. For a given keyword query, the authors first generate candidate networks and also produce connected tuple trees according to the generated candidate networks by reducing the size of intermediate joining results. They then model the generated connected tuple trees as a document and evaluate score for each document to estimate its relevance. Finally, the authors retrieve top-k keyword queries by ranking the results. In this paper, the authors propose a new ranking method based on virtual document. They also propose Top-k CTT algorithm by using the frequency threshold value. The experimental results are shown by comparison of the proposed ranking method and the previous ranking methods on IMDB and DBLP datasets.

Research on keyword search in database community has achieved a lot of success, and areas of interests have been moved from keyword search in relational databases to various advanced issues such as keyword search in multimedia data and data streams. Yet, many fundamental issues on keyword search in traditional databases remain. One such issue is how to interpret users' information needs behind keywords they provided. A common approach of many prototype systems is to make such interpretation as a designer's choice (such as imposing AND or OR semantics, or a combination), leaving no choice to the users. A much more meaningful approach would be allowing users themselves to specify the required semantics through contextual information. So can we build a system which stays with the simplicity of Keyword search, yet can incorporate the contextual information provided in the user query? In this paper we introduce STRUCT to explore this idea. STRUCT takes English language queries involving intended keywords; we refer to such search as English query search. Instead of resorting on a full-fledged natural language processing, the unneeded words in the queries are discarded. Only the specific contextual information along with the keywords containing database contents will be used to construct SQL queries. The contextual information is used to interpret the meaning of the queries, including the semantics involving AND, OR and NOT. In this paper we describe the architecture of STRUCT, the procedure of English query processing (parsing), the basic idea of the grouping algorithm, SQL query construction and sample results of experiments.

Keyword search in relational databases has recently emerged as a new research topic. As a search result is often assembled from multiple relational tables, existing IR-style ranking strategies can not be applied directly. In this paper, we propose a novel IR ranking strategy considering query semantics for effective keyword search. The experimental results on a large-scale real database demonstrate that our method results in significant improvement in terms of retrieval effectiveness as compared to previous ranking strategies.

the massive volume of data in databases, web pages, and document files usually causes information to be disorganized and unclear for the user. Therefore, information in such an environment can be classified into three forms: structured, semistructured, or unstructured. Structured information is the best form of information because it facilitates the acquisition and comprehension of knowledge. Relational Database Management System (RDBMS) has a robust structure that manages, organizes and retrieves data. There are many attempts have been made in order to deal with such data. These attempts can be categorized into three groups: within a database schema, by a developed data model within the database, or by query-based techniques in database. Nonetheless, RDBMS contain massive amount of unstructured data such as textual data.. This paper proposed Textual Virtual Schema Model (TVSM). TVSM is conducted to perform semantic textual data linking and clustering and is embedded in the relational database structure (schema). In addition, linking and converting the unstructured information to structured data. Quality improvement of textual data clusters. Achievement of high query processing efficiency in retrieving data clusters. TVSM initially developed to assist researchers, developers, and database administrators who are concerned on unstructured information management, information extraction, multi-document clustering, information retrieval, query processing efficiency, personal information management, question answering, information integration, news tracking, and news summarization

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.

Call me Ishmael. We are about to learn about the White Whale: the Relational Database Management System (RDBMS). RDBMS is the most common type of data storage in business today, because it is a proven and well-supported technology. In this chapter, I will discuss what a relational database is and how you can use one. I will try to touch on most aspects of the relational database, but a relational database is too large of a whale to cover in one chapter.

Most of commercial websites provide customers with menu-driven navigation and keyword search. However, these inconvenient interfaces increase the number of mouse clicks and decrease customers' interest in surfing the websites. To resolve the problem, we propose an information retrieval assistant using a natural language interface in online sales domains. The information retrieval assistant has a client-server structure; a system connector and a NLP (natural language processing) server. The NLP server performs a linguistic analysis of users' queries with the help of coordinated NLP agents that are based on shallow NLP techniques. After receiving the results of the linguistic analysis from the NLP server, the system connector interacts with outer information provision systems such as conventional information retrieval systems and relational database management systems according to the analysis results. Owing to the client-server structure, we can easily add other information provision systems to the information retrieval assistant with trivial modifications of the NLP server. In addition, the information retrieval assistant guarantees fast responses because it uses shallow NLP techniques. In the preliminary experiment, as compared to the menu-driven system, we found that the information retrieval assistant could reduce the bothersome tasks such as menu selecting and mouse clicking because it provides a convenient natural language interface.

In the last two decades, unstructured information has become a major challenge in information management. Such challenge is caused by the massive and increasing amount of information resulting from the conversion of almost all daily tasks into digital format. Tools and applications are necessary in organizing unstructured information, which can be found in structured data, such as in relational database management systems (RDBMS). RDBMS has robust and powerful structures for managing, organizing, and retrieving data. However, structured data still contains unstructured information. In this paper, the methods used for managing unstructured data in RDBMS are investigated. In addition, an incremental and dynamic repository for managing unstructured data in relational databases are introduced. The proposed technique organizes unstructured information through linkages among textual data based on semantics. Furthermore, it provides users with a good picture of the unstructured information. The proposed technique can rapidly and easily obtain useful data, and thus, it can be applied in numerous domains, particularly those who deal with textual data, such as news articles.KeywordsRelational databasesUnstructured dataDocument clusteringQuery efficiencyTextual data

Both object-oriented and relational database management systems are referred to as DBMS. The web-scale is expanding at an incredible rate, and with it comes an increase in mobile applications, technologies, and social media platforms. The rapid growth of these technologies produces a variety of structured and unstructured data in various forms, each of which has various consequences for data security. Using RDBMS and OODBMS, this study's methodology outlines the development, installation, and management process of multiple and complicated data sets. This study gives a documented study comparing different kinds of data stored in databases by employing two types of database techniques: relational database method and object-oriented database technique. We use a different fingerprint database for the web and mobile devices. This database is also managed using different technologies, such as MySQL, and it accommodates data generated using distinct methods. In addition, this study explores the benefits and advantages of the various databases and determines the ideal database management system for organizations. According to the survey's findings, RDBMS stores data and the relationships between them in tables, but OODBMS stores data in objects, in a manner analogous to Object-Oriented Programming (OOP). According to the study's findings, the primary distinction between relational database management systems (RDBMS) and object-oriented database management systems (OODBMS) is that RDBMS is a Database Management System based on the relational model. In contrast, OODBMS is a Database Management System that facilitates creating and modeling data as objects.

It has become desirable to provide a way of keyword search for users to query structured information in an XML database (data-centric retrieval) by combining database and information retrieval techniques. Therefore, the key challenges of keyword search in the XML database are how to define appropriate result models meeting user’s search intents, how to search the results by using efficient algorithms, and how to ranking the results. In this chapter, on one hand, the authors present the foundational knowledge of XML keyword search such as XML data models, XML query languages, inverted index, and Dewey encoding. On the other hand, some existing typical researches of keyword search in XML are presented, including the results models such as Smallest Lowest Common Ancestor (SLCA), Exclusive Lowest Common Ancestor (ELCA), Meaningful Lowest Common Ancestor (MLCA), the related search algorithms, and the ranking approaches.

The project entitled as Big Data, Internet of Things, and Mobile Devices, in Portuguese Banco de Dados, Internet das Coisas e Dispositivos Moveis (BDIC-DM) was implemented at the Brazilian Aeronautics Institute of Technology (ITA) on the 1st Semester of 2015. It involved 60 graduate students within just 17 academic weeks. As a starting point for some features of real time Online Transactional Processing (OLTP) system, the Relational Database Management System (RDBMS) MySQL was used along with the NoSQL Cassandra to store transaction data generated from web portal and mobile applications. Considering batch data analysis, the Apache Hadoop Ecosystem was used for Online Analytical Processing (OLAP). The infrastructure based on the Apache Sqoop tool has allowed exporting data from the relational database MySQL to the Hadoop File System (HDFS), while Python scripts were used to export transaction data from the NoSQL database to the HDFS. The main objective of the BDIC-DM project was to implement an e-Commerce prototype system to manage credit card transactions, involving large volumes of data, by using different technologies. The used tools involved generation, storage, and consumption of Big Data. This paper describes the process of integrating NoSQL and relational database with Hadoop Cluster, during an academic project using the Scrum Agile Method. At the end, processing time significantly decreased, by using appropriate tools and available data. For future work, it is suggested the investigation of other tools and datasets.

Aufgrund fallender Preise zur Speicherung von Daten kann man derzeit eine explosionsartige Zunahme in der Menge der verfügbaren Daten beobachten. Diese Entwicklung gibt Unternehmen und wissenschaftliche Institutionen die Möglichkeit empirische Daten in ungekannter Größenordnung zu analysieren. Für viele Firmen ist die Analyse der gesammelten Daten aus ihrem operationalen Geschäft längst zu einem zentralen strategischen Aspekt geworden. Im Gegensatz zu der seit längerem schon betriebenen Business Intelligence, bestehen diese Analysen nicht mehr nur aus traditionellen relationalen Anfragen. In zunehmendem Anteil kommen komplexe Algorithmen aus den Bereichen Data Mining und Maschinelles Lernen hinzu, um versteckte Muster in den Daten zu erkennen, oder Vorhersagemodelle zu trainieren. Mit zunehmender Datenmenge und Komplexität der Analysen wird jedoch eine neue Generation von Systemen benötigt, die diese Kombination aus Anfragekomplexität und Datenvolumen gewachsen sind. Relationale Datenbanken waren lange Zeit das Zugpferd der Datenanalyse im großen Stil. Grund dafür war zum großen Teil ihre deklarativen Anfragesprache, welche es ermöglichte die logischen und physischen Aspekte der Datenspeicherung und Verarbeitung zu trennen, und Anfragen automatisch zu optimieren. Das starres Datenmodell und ihre beschränkte Menge von möglichen Operationen schränken jedoch die Anwendbarkeit von relationalen Datenbanken für viele der neueren analytischen Probleme stark ein. Diese Erkenntnis hat die Entwicklung einer neuen Generation von Systemen und Architekturen eingeläutet, die sich durch sehr generische Abstraktionen für parallelisierbare analytische Programme auszeichnen; MapReduce kann hier beispielhaft genannt werden, als der zweifelsohne prominenteste Vertreter dieser Systeme. Zwar vereinfachte und erschloss diese neue Generation von Systemen die Datenanalyse in diversen neuen Anwendungsfeldern, sie ist jedoch nicht in der Lage komplexe Anwendungen aus den Bereichen Data Mining und Maschinelles Lernen effizient abzubilden, ohne sich dabei extrem auf spezifische Anwendungen zu spezialisieren. Verglichen mit den relationalen Datenbanken haben MapReduce und vergleichbare Systeme außerdem die deklarative Abstraktion aufgegeben und zwingen den Anwender dazu systemnahe Programme zu schreiben und diese manuell zu optimieren. In dieser Dissertation werden verschiedene Techniken vorgestellt, die es ermöglichen etliche der zentralen Eigenschaften von relationalen Datenbanken im Kontext dieser neuen Generation von daten-parallelen Analysesystemen zu realisieren. Mithilfe dieser Techniken ist es möglich ein Analysesystem zu beschreiben, dessen Programme gleichzeitig sowohl generische und ausdrucksstark, als auch prägnant und deklarativ sind. Im einzelnen stellen wir folgende Techniken vor: Erstens, eine Programmierabstraktion die generisch ist und mit komplexen Datenmodellen umgehen kann, aber gleichzeitig viele der deklarativen Eigenschaften der relationalen Algebra erhält. Programme, die gegen dies Abstraktion entwickelt werden können ähnlich optimiert werden wie relationale Anfragen. Zweitens stellen wir eine Abstraktion für iterative daten-parallele Algorithmen vor. Die Abstraktion unterstützt inkrementelle (delta-basierte) Berechnungen und geht mit zustandsbehafteteten Berechnungen transparent um. Wir beschreiben wie man einen relationalen Anfrageoptimierer erweitern kann so dass dieser iterative Anfragen effektiv optimiert. Wir zeigen dabei dass der Optimierer dadurch in die Lage versetzt wird automatisch Ausführungspläne zu erzeugen, die wohlbekannten, manuell erstellten Programmen entsprechen. Die Abstraktion subsumiert dadurch spezialisierte Systeme (wie Pregel) und bietet vergleichbare Performanz. Drittens stellen wir Methoden vor, um die Programmierabstraktion in eine funktionale Sprachen einzubetten. Diese Integration ermögliche es prägnante Programme zu schreiben und einfach wiederzuverwendenden Komponenten und Bibliotheken, sowie Domänenspezifische Sprachen, zu erstellen. Wir legen dar wie man die Übersetzung und Optimierung des daten-parallelen Programms mit dem Sprachübersetzer der funktionalen Sprache so integriert, dass maximales Optimierungspotenzial besteht.

Translating object-oriented database transactions into relational transactions