Relational databases are built on Relational Model proposed by Dr. E. F. Codd. The relational model has become a consistent and widely used DBMS in the world. The databases in this model are efficient in storing and retrieval of data besides providing authentication through credentials. However, there might be many other attacks apart from stealing credentials and intruding database. Adversaries may always try to intrude into the relational database for monetary or other gains [1]. The relational databases are subjected to malicious attacks as they hold the valuable business data which is sensitive in nature. Monitoring such database continuously is a task which is inevitable keeping the importance of database in mind. This is a strategy that is in top five database strategies as identified by Gartner research which are meant for getting rid of data leaks in organizations [2]. There are regulations from governments like US with respect to managing data securely. The data management like HIAPP, GLBA, and PCI etc. is mentioned in the regulations as examples. / Intrusion detection systems play an important role in detecting online intrusions and provide necessary alerts. Intrusion detection can also be done for relational databases. Intrusion response system for a relational database is essential to protect it from external and internal attacks. We propose a new intrusion response system for relational databases based on the database response policies. We have developed an interactive language that helps database administrators to determine the responses to be provided by the response system based on the malicious requests encountered by relational database. We also maintain a policy database that maintains policies with respect to response system. For searching the suitable policies algorithms are designed and implemented. Matching the right policies and policy administration are the two problems that are addressed in this paper to ensure faster action and prevent any malicious changes to be made to policy objects. Cryptography is also used in the process of protecting the relational database from attacks. The experimental results reveal that the proposed response system is effective and useful.

In the presented work we study evolution of a database schema and its impact on related issues. The work contains a review of important problems related to the change in a respective storage of the data. It describes existing approaches of these problems as well. In detail the work analyzes an impact of database schema changes on database queries, which relate to the particular database schema. The approach presented in this thesis shows a ability to model database queries together with a database schema model. The thesis describes a solution how to adapt database queries related to the evolved database schema. Finally the work contains a number of experiments that verify a proposal of the presented solution.

This thesis presents an implementation of a learning classifier system which learns good database schema. The system is implemented in Java using the NetBeans development environment, which provides a good control for the GUI components. The system contains four components: a user interface, a rule and message system, an apportionment of credit system, and genetic algorithms. The input of the system is a set of simple database schemas and the objective for the classifier system is to keep the good database schemas which are represented by classifiers. The learning classifier system is given some basic knowledge about database concepts or rules. The result showed that the system could decrease the bad schemas and keep the good ones.

The problem of functional annotation of novel sequences has been a sigfinicant issue for many laboratories that decided to apply next generation sequencing techniques to less studied species. In particular experiments such as transcriptome analysis heavily suer from this problem due to the impossibility of ascribing their results in a relevant biological context. Several tools have been proposed to solve this problem through homology annotation transfer. The principle behind this strategy is that homologous genes share common functions in dierent organisms, and therefore annotations are transferable between these genes. Commonly, BLAST reports are used to identify a suitable homologousgene in a well annotated species and the annotation is then transferred fromthe homologue to the novel sequence. Not all homologues, however, possess valid functional annotations. The aim of this project was to devise an algorithm to process BLAST reports and provide a criterion to discriminate between homologues with a biologically informative and uninformative annotation, respectively. In addition, all data obtained from the BLAST report isto be stored in a relational database for ease of consultation and visualization. In order to test the solidity of the system, we utilized 750 novel sequences obtained through application of next generation sequencing techniques to Avena sativa samples. This species particularly suits our needs as it represents the typical target for homology annotation transfer: lack of a reference genome and diculty in attributing functional annotation. The system was able to perform all the required tasks. Comparisons between best hits asdetermined by BLAST and best hits as determined by the algorithm showed a significant increase in the biological significance of the results when thealgorithm sorting system was applied.

With the growing of Internet, people use network frequently. Many PC applications have moved to the network-based environment, such as text processing, calendar, photo management, and even users can develop applications on the network. Google is a company providing web services. Its popular services are search engine and Gmail which attract people by short response time and lots amount of data storage. It also charges businesses to place their own advertisements. Another hot social network is Facebook which is also a popular website. It processes huge instant messages and social relationships between users. The magic power of doing this depends on the new technique, Cloud Computing. Cloud computing has ability to keep high-performance processing and short response time, and its kernel components are distributed data storage and distributed data processing. Hadoop is a famous open source to build cloud distributed file system and distributed data analysis. Hadoop is suitable for batch applications and write-once-and-read-many applications. Thus, currently there are only fewer applications, such as pattern searching and log file analysis, to be implemented over Hadoop. However, almost all database applications are still using relational databases. To port them into cloud platform, it becomes necessary to let relational database running over HDFS. So we will test the solution of FUSE-DFS which is an interface to mount HDFS into a system and is used like a local filesystem. If we can make FUSE-DFS performance satisfy user¡¦s application, then we can easier persuade people to port their application into cloud platform with least overhead.

Master of Science / Department of Computer Science / Doina Caragea / An Introduction to Databases course involves learning the concepts of data storage, manipulation, and retrieval. Relational databases provide an ideal learning path for understanding database concepts. The Structured Query Language (SQL) is a standard language for interacting with relational database. Each database vendor implements a variation of the SQL standard. Furthermore, a particular question that asks for some data can be written in many ways, using somewhat similar or structurally different SQL queries. Evaluation of SQL queries for correctness involves the verification of the SQL syntax and semantics, as well as verification of the output of queries and the usage of correct clauses. An evaluation tool should be independent of the specific database queried, and of the nature of the queries, and should allow multiple ways of providing input and retrieving the output. In this report, we have developed an evaluation tool for SQL queries, which checks for correctness of MySQL and PostgreSQL queries with the help of a parser that can identify SQL clauses. The tool developed will act as a portal for students to test and improve their queries, and finally to submit the queries for grading. The tool minimizes the manual effort required while grading, by taking advantage of the SQL parser to check queries for correctness, provide feedback, and allow submission.

Research in natural language processing (NLP) focuses recently on the development of learned language models called word embedding models like word2vec, fastText, and BERT. Pre-trained on large amounts of unstructured text in natural language, those embedding models constitute a rich source of common knowledge in the domain of the text used for the training. In the NLP community, significant improvements are achieved by using those models together with deep neural network models. To support applications to benefit from word embeddings, we extend the capabilities of traditional relational database systems, which are still by far the most common DBMSs but only provide limited text analysis features. Therefore, we implement (a) novel database operations involving embedding representations to allow a database user to exploit the knowledge encoded in word embedding models for advanced text analysis operations. The integration of those operations into database query language enables users to construct queries using novel word embedding operations in conjunction with traditional query capabilities of SQL. To allow efficient retrieval of embedding representations and fast execution of the operations, we implement (b) novel search algorithms and index structures for approximated kNN-Joins and integrate those into a relational database management system. Moreover, we investigate techniques to optimize embedding representations of text values in database systems. Therefore, we design (c) a novel context adaptation algorithm. This algorithm utilizes the structured data present in the database to enrich the embedding representations of text values to model their context-specific semantic in the database. Besides, we provide (d) support for selecting a word embedding model suitable for a user's application. Therefore, we developed a data processing pipeline to construct a dataset for domain-specific word embedding evaluation. Finally, we propose (e) novel embedding techniques for pre-training on tabular data to support applications working with text values in tables. Our proposed embedding techniques model semantic relations arising from the alignment of words in tabular layouts that can only hardly be derived from text documents, e.g., relations between table schema and table body. In this way, many applications, which either employ embeddings in supervised machine learning models, e.g., to classify cells in spreadsheets, or through the application of arithmetic operations, e.g., table discovery applications, can profit from the proposed embedding techniques.:1 INTRODUCTION 1.1 Contribution 1.2 Outline 2 REPRESENTATION OF TEXT FOR NATURAL LANGUAGE PROCESSING 2.1 Natural Language Processing Systems 2.2 Word Embedding Models 2.2.1 Matrix Factorization Methods 2.2.2 Learned Distributed Representations 2.2.3 Contextualize Word Embeddings 2.2.4 Advantages of Contextualize and Static Word Embeddings 2.2.5 Properties of Static Word Embeddings 2.2.6 Node Embeddings 2.2.7 Non-Euclidean Embedding Techniques 2.3 Evaluation of Word Embeddings 2.3.1 Similarity Evaluation 2.3.2 Analogy Evaluation 2.3.3 Cluster-based Evaluation 2.4 Application for Tabular Data 2.4.1 Semantic Search 2.4.2 Data Curation 2.4.3 Data Discovery 3 SYSTEM OVERVIEW 3.1 Opportunities of an Integration 3.2 Characteristics of Word Vectors 3.3 Objectives and Challenges 3.4 Word Embedding Operations 3.5 Performance Optimization of Operations 3.6 Context Adaptation 3.7 Requirements for Model Recommendation 3.8 Tabular Embedding Models 4 MANAGEMENT OF EMBEDDING REPRESENTATIONS IN DATABASE SYSTEMS 4.1 Integration of Operations in an RDBMS 4.1.1 System Architecture 4.1.2 Storage Formats 4.1.3 User-Defined Functions 4.1.4 Web Application 4.2 Nearest Neighbor Search 4.2.1 Tree-based Methods 4.2.2 Proximity Graphs 4.2.3 Locality-Sensitive Hashing 4.2.4 Quantization Techniques 4.3 Applicability of ANN Techniques for Word Embedding kNN-Joins 4.4 Related Work on kNN Search in Database Systems 4.5 ANN-Joins for Relational Database Systems 4.5.1 Index Architecture 4.5.2 Search Algorithm 4.5.3 Distance Calculation 4.5.4 Optimization Capabilities 4.5.5 Estimation of the Number of Targets 4.5.6 Flexible Product Quantization 4.5.7 Further Optimizations 4.5.8 Parameter Tuning 4.5.9 kNN-Joins for Word2Bits 4.6 Evaluation 4.6.1 Experimental Setup 4.6.2 Influence of Index Parameters on Precision and Execution Time 4.6.3 Performance of Subroutines 4.6.4 Flexible Product Quantization 4.6.5 Accuracy of the Target Size Estimation 4.6.6 Performance of Word2Bits kNN-Join 4.7 Summary 5 CONTEXT ADAPTATION FOR WORD EMBEDDING OPTIMIZATION 5.1 Related Work 5.1.1 Graph and Text Joint Embedding Methods 5.1.2 Retrofitting Approaches 5.1.3 Table Embedding Models 5.2 Relational Retrofitting Approach 5.2.1 Data Preparation 5.2.2 Relational Retrofitting Problem 5.2.3 Relational Retrofitting Algorithm 5.2.4 Online-RETRO 5.3 Evaluation Platform: Retro Live 5.3.1 Functionality 5.3.2 Interface 5.4 Evaluation 5.4.1 Datasets 5.4.2 Training of Embeddings 5.4.3 Machine Learning Models 5.4.4 Evaluation of ML Models 5.4.5 Run-time Measurements 5.4.6 Online Retrofitting 5.5 Summary 6 MODEL RECOMMENDATION 6.1 Related Work 6.1.1 Extrinsic Evaluation 6.1.2 Intrinsic Evaluation 6.2 Architecture of FacetE 6.3 Evaluation Dataset Construction Pipeline 6.3.1 Web Table Filtering and Facet Candidate Generation 6.3.2 Check Soft Functional Dependencies 6.3.3 Post-Filtering 6.3.4 Categorization 6.4 Evaluation of Popular Word Embedding Models 6.4.1 Domain-Agnostic Evaluation 6.4.2 Evaluation of a Single Facet 6.4.3 Evaluation of an Object Set 6.5 Summary 7 TABULAR TEXT EMBEDDINGS 7.1 Related Work 7.1.1 Static Table Embedding Models 7.1.2 Contextualized Table Embedding Models 7.2 Web Table Embedding Model 7.2.1 Preprocessing 7.2.2 Text Serialization 7.2.3 Encoding Model 7.2.4 Embedding Training 7.3 Applications for Table Embeddings 7.3.1 Table Union Search 7.3.2 Classification Tasks 7.4 Evaluation 7.4.1 Intrinsic Evaluation 7.4.2 Table Union Search Evaluation 7.4.3 Table Layout Classification 7.4.4 Spreadsheet Cell Classification 7.5 Summary 8 CONCLUSION 8.1 Summary 8.2 Directions for Future Work BIBLIOGRAPHY LIST OF FIGURES LIST OF TABLES A CONVEXITY OF RELATIONAL RETROFITTING B EVALUATION OF THE RELATIONAL RETROFITTING HYPERPARAMETERS

Ontology, an explicit specification of a shared conceptualisation, captures knowledge about a specific domain of interest. The realisation of ontologies, revolutionised the way data stored in relational databases is accessed and manipulated through ontology and database integration. When integrating ontologies with relational databases, several choices exist regarding aspects such as database implementation, ontology language features, and mappings. However, it is unclear which aspects are relevant and when they affect specific choices. This imposes difficulties in deciding which choices to make and their implications on ontology and database integration solutions. Within this study, a decision-making tool that guides users when selecting a technology and developing a solution that integrates ontologies with relational databases is developed. A theory analysis is conducted to determine current status of technologies that integrate ontologies with databases. Furthermore, a theoretical study is conducted to determine important features affecting ontology and database integration, ontology language features, and choices that one needs to make given each technology. Based on the building blocks stated above, an artifact-building approach is used to develop the decision-making tool, and this tool is verified through a proof-of-concept to prove the usefulness thereof. Key terms: Ontology, semantics, relational database, ontology and database integration, mapping, Web information portal. / Information Science / M. Sc. (Information Systems)

Developing interactive web application systems requires a large amount of effort on designing database, system logic and user interface. These tasks are expensive and error-prone. Web application systems are accessed and used by many different sets of people with different backgrounds and numerous demands. Meeting these demands requires frequent updating for Web application systems which results in a very high cost process. Thus, many attempts have been made to automate, to some degree, the construction of Web user interfaces. Three main directions have been cited for this purpose. The first direction suggested of generating user interfaces from the application's data model. This path was able to generate the static layout of user interfaces with dynamic behaviour specified programmatically. The second tendency suggested deployment of the domain model to generate both, the layout of a user interface and its dynamic behaviour. Web applications built based on this approach are most useful for domain-specific interfaces with a relatively fixed user dialogue. The last direction adopted the notion of deploying database metadata to developing dynamic user interfaces. Although the notion was quite valuable, its deployment did not present a generic solution for generating a variety of types of dynamic Web user interface targeting several platforms and electronic devices. This thesis has inherited the latter direction and presented significant improvements on the current deployment of this tendency. This thesis aims to contribute towards the development of an abstract framework to generate abstract and dynamic Web user interfaces not targeted to any particular domain or platform. To achieve this target, the thesis proposed and evaluates a general notion for implementing a prototype system that uses an internal model (i.e. database metadata) in conjunction with XML technology. Database metadata is richer than any external model and provides the information needed to build dynamic user interfaces. In addition, XML technology became the mainstream of presenting and storing data in an abstract structure. It is widely adopted in Web development society because of its ability to be transformed into many different formats with a little bit of effort. This thesis finds that only Java can provide us with a generalised database metadata based framework. Other programming languages apply some restrictions on accessing and extracting database metadata from numerous database management systems. Consequently, JavaServlets and relational database were used to implement the proposed framework. In addition, Java Data Base Connectivity was used to bridge the two mentioned technologies. The implementation of our proposed approach shows that it is possible and very straightforward to produce different automatic and dynamic Web entry forms that not targeted at any platform. In addition, this approach can be applied to a particular domain without affecting the main notion or framework architecture. The implemented approach demonstrates a number of advantages over the other approaches based on external or internal models.

Ontologijos tampa vis populiaresnės, nes leidžia organizacijoms lanksčiau aprašyti dalykinę sritį, ieškoti informacijos iš skirtingų šaltinių ir pateikti semantiškai tiksliau atrinktus rezultatus vartotojams. Tačiau, augant informacijos ontologijoje kiekiui, saugoti ją tekstiniame faile tampa neefektyvu. Šio tyrimo tikslas yra pagerinti užklausų vykdymo ontologijoje galimybes, kai jos saugojamos reliacinėje duomenų bazėje. Tam buvo sukurtas SPARQL užklausų vykdymo metodas ontologijoje, saugojamoje reliacinėje duomenų bazėje pagal OWL2RDB algoritmą. Eksperimentas patvirtino, kad esant dideliam individų skaičiui, metodas leidžia greičiau vykdyti užklausas reliacinėje duomenų bazėje saugojamoje ontologijoje, negu ontologijoje, saugojamoje tekstiniame faile. / Ontologies are becoming increasingly popular because they allow organizations to describe their problem domains in a more flexible manner and to search information from multiple sources giving semantically significant results for users. However, the increasing amount of information in ontology makes its storing in a text file not effective. The aim of this research is to improve possibilities of querying large ontologies when these are kept in relational databases. The method was created for executing SPARQL queries in ontology, stored in a relational database created by OWL2RDB algorithm. Experiments have shown that the method improves query performance time in comparison with existing query engine especially for large ontologies having many individuals.