The thesis of this research is that database queries can be translated to corresponding English descriptions for the use in applications in intelligent tutoring (in particular, problem generation and feedback generation) for the subject domain of database query construction. To demonstrate this thesis, a rule-based graph-rewriting algorithm and a concrete set of rules for systematically transforming queries in a subset of SQL to English descriptions are presented. Further, through an implementation of this technique, this study demonstrates an evaluation of its performance on SQL queries from database textbooks.

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Published Article / To enable meaningful and consistent communication between different software systems in a particular domain (such as manufacturing, law or medicine), a standardised vocabulary and communication language is required by all the systems involved. Concepts in the domain about which the systems want to communicate are formalized in an ontology by establishing the meaning of concepts and creating relationships between them. The inputs to this process in found by analysing the physical domain and its processes. The resulting ontology structure is a computer useable representation of the physical domain about which the systems want to communicate. To enable the long term persistence of the actual data contained in these concepts and the enforcement of various business rules, a sufficiently powerful database system is required. This paper presents the design of a manufacturing ontology and its accompanying relational database schema that will be used in a manufacturing test domain.

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Graph data is becoming more and more pervasive. Indeed, services such as Social Networks or the Semantic Web can no longer rely on the traditional relational model, as its structure is somewhat too rigid for the applications they have in mind. For this reason we have seen a continuous shift towards more non-standard models. First it was the semi-structured data in the 1990s and XML in 2000s, but even such models seem to be too restrictive for new applications that require navigational properties naturally modelled by graphs. Social networks fit into the graph model by their very design: users are nodes and their connections are specified by graph edges. The W3C committee, on the other hand, describes RDF, the model underlying the Semantic Web, by using graphs. The situation is quite similar with crime detection networks and tracking workflow provenance, namely they all have graphs inbuilt into their definition. With pervasiveness of graph data the important question of querying and maintaining it has emerged as one of the main priorities, both in theoretical and applied sense. Currently there seem to be two approaches to handling such data. On the one hand, to extract the actual data, practitioners use traditional relational languages that completely disregard various navigational patterns connecting the data. What makes this data interesting in modern applications, however, is precisely its ability to compactly represent intricate topological properties that envelop the data. To overcome this issue several languages that allow querying graph topology have been proposed and extensively studied. The problem with these languages is that they concentrate on navigation only, thus disregarding the data that is actually stored in the database. What we propose in this thesis is the ability to do both. Namely, we will study how query languages can be designed to allow specifying not only how the data is connected, but also how data changes along paths and patterns connecting it. To this end we will develop several query languages and show how adding different data manipulation capabilities and different navigational features affects the complexity of main reasoning tasks. The story here is somewhat similar to the early success of the relational data model, where theoretical considerations led to a better understanding of what makes certain tasks more challenging than others. Here we aim for languages that are both efficient and capable of expressing a wide variety of queries of interest to several groups of practitioners. To do so we will analyse how different requirements affect the language at hand and at the end provide a good base of primitives whose inclusion into a language should be considered, based on the applications one has in mind. Namely, we consider how adding a specific operation, mechanism, or capability to the language affects practical tasks that such an addition plans to tackle. In the end we arrive at several languages, all of them with their pros and cons, giving us a good overview of how specific capabilities of the language affect the design goals, thus providing a sound basis for practitioners to choose from, based on their requirements.

Context. The present trend in a large variety of applications are ranging from the web and social networking to telecommunications, is to gather and process very large and fast growing amounts of information leading to a common set of problems known collectively as “Big Data”. The ability to process large scale data analytics over large number of data sets in the last decade proved to be a competitive advantage in a wide range of industries like retail, telecom and defense etc. In response to this trend, the research community and the IT industry have proposed a number of platforms to facilitate large scale data analytics. Such platforms include a new class of databases, often refer to as NoSQL data stores. Apache Cassandra is a type of NoSQL data store. This research is focused on analyzing the performance of different compaction strategies in different use cases for default Cassandra stress model. Objectives. The performance of compaction strategies are observed in various scenarios on the basis of three use cases, Write heavy- 90/10, Read heavy- 10/90 and Balanced- 50/50. For a default Cassandra stress model, so as to finally provide the necessary events and specifications that suggest when to switch from one compaction strategy to another. Methods. Cassandra single node network is deployed on a web server and its behavior of read and write performance with different compaction strategies is studied with read heavy, write heavy and balanced workloads. Its performance metrics are collected and analyzed. Results. Performance metrics of different compaction strategies are evaluated and analyzed. Conclusions. With a detailed analysis and logical comparison, we finally conclude that Level Tiered Compaction Strategy performs better for a read heavy (10/90) workload while using default Cassandra stress model , as compared to size tiered compaction and date tiered compaction strategies. And for Balanced Date tiered compaction strategy performs better than size tiered compaction strategy and date tiered compaction strategy.

published_or_final_version / abstract / Computer Science / Doctoral / Doctor of Philosophy

published_or_final_version / Computer Science / Master / Master of Philosophy

No description available.

No description available.