A multi-tenant database framework for software and cloud computing applications

University of Technology, Sydney. Faculty of Engineering and Information Technology.Cloud Computing is a new computing paradigm that transforms accessing computing resources from internal data centres to external service providers. This approach is rapidly becoming a standard for offering cost effective and elastic computing services that are used over the internet. Software as a service (SaaS) is one of the Cloud Computing service models that exploits economies of scale for SaaS service providers by offering the same software and computing environment for multiple tenants. This contemporary multi-tenant service requires a multi-tenant database design that can accommodate data for multiple tenants in one single database schema. Due to multi-tenant database resource sharing in this service, the multi-tenant schema should be highly secured, optimized, configurable, and extendable during runtime execution to fulfil the applications’ requirements of different tenants. However, traditional Relational Database Management Systems (RDBMS) do not support such multi-tenant database schema capabilities, and it is a significant challenge to enable RDBMS to support these capabilities. Therefore, one solution is using an intermediate software layer that mediates multi-tenant applications and RDBMS, to convert multi-tenant queries into regular database queries, and to execute them in a RDBMS. Developing such a multi-tenant software layer to manage and access tenants’ data is a hard and complex problem to solve and has significant complexities that involve longer development lifecycle. There are two main contributions of this thesis. Firstly, a proposal for a novel multi-tenant schema technique called Elastic Extension Tables (EET). Secondly, a proposal for a multi-tenant database framework prototype to implement EET schema in a RDBMS. This approach can be used to develop a software layer that mediates software applications and a RDBMS. This software layer aims to facilitate the development of software applications, and multi-tenant SaaS and Big Data applications for both cloud service providers and their tenants. Extensive experiments were conducted to evaluate the feasibility and effectiveness of EET multi-tenant database schema by comparing it with Universal Table Schema Mapping (UTSM), which is commercially used. Significant performance improvements obtained using EET when compared to UTSM, makes the EET schema a good candidate for implementing multi-tenant databases and multi-tenant applications. Furthermore, the prototype of the EET framework was developed, and several experiments were performed to verify the practicability and the effectiveness of using this framework that based on EET multi-tenant database schema. The results of the experiments indicate that the EET framework is suitable for the development of software applications in general, and multi-tenant SaaS and Big Data applications in particular

Database independent Migration of Objects into an Object-Relational Database

This paper reports on the CERN-based WISDOM project which is studying the serialisation and deserialisation of data to/from an object database (objectivity) and ORACLE 9i.Comment: 26 pages, 18 figures; CMS CERN Conference Report cr02_01

Innovative Evaluation System – IESM: An Architecture for the Database Management System for Mobile Application

As the mobile applications are constantly facing a rapid development in the recent years especially in the academic environment such as student response system [1-8] used in universities and other educational institutions; there has not been reported an effective and scalable Database Management System to support fast and reliable data storage and retrieval. This paper presents Database Management Architecture for an Innovative Evaluation System based on Mobile Learning Applications. The need for a relatively stable, independent and extensible data model for faster data storage and retrieval is analyzed and investigated. It concludes by emphasizing further investigation for high throughput so as to support multimedia data such as video clips, images and documents

Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions

RDFViewS: A Storage Tuning Wizard for RDF Applications

In recent years, the significant growth of RDF data used in numerous applications has made its efficient and scalable manipulation an important issue. In this paper, we present RDFViewS, a system capable of choosing the most suitable views to materialize, in order to minimize the query response time for a specific SPARQL query workload, while taking into account the view maintenance cost and storage space constraints. Our system employs practical algorithms and heuristics to navigate through the search space of potential view configurations, and exploits the possibly available semantic information - expressed via an RDF Schema - to ensure the completeness of the query evaluation

MonetDB/XQuery: a fast XQuery processor powered by a relational engine

Relational XQuery systems try to re-use mature relational data management infrastructures to create fast and scalable XML database technology. This paper describes the main features, key contributions, and lessons learned while implementing such a system. Its architecture consists of (i) a range-based encoding of XML documents into relational tables, (ii) a compilation technique that translates XQuery into a basic relational algebra, (iii) a restricted (order) property-aware peephole relational query optimization strategy, and (iv) a mapping from XML update statements into relational updates. Thus, this system implements all essential XML database functionalities (rather than a single feature) such that we can learn from the full consequences of our architectural decisions. While implementing this system, we had to extend the state-of-the-art with a number of new technical contributions, such as loop-lifted staircase join and efficient relational query evaluation strategies for XQuery theta-joins with existential semantics. These contributions as well as the architectural lessons learned are also deemed valuable for other relational back-end engines. The performance and scalability of the resulting system is evaluated on the XMark benchmark up to data sizes of 11GB. The performance section also provides an extensive benchmark comparison of all major XMark results published previously, which confirm that the goal of purely relational XQuery processing, namely speed and scalability, was met