Listen to the customer: Model-driven database design

In modern IT landscapes, databases are subject to a major role change. Especially in Service-Oriented Architectures, databases are more and more frequently dedicated to a single application. Therefore, it is even more important to reflect the application requirements in their design. Software developers and application experts formulate application requirements in software models. Hence, we obviously need to bridge the gap to the software world and directly derive a database design from the software models used in application development and maintenance. We introduce this concept as model-driven database design. In this paper, we present the architecture principles of a model-driven database design tool and details on the enumeration and evaluation of logical database designs

MaxPart: An Efficient Search-Space Pruning Approach to Vertical Partitioning

Vertical partitioning is the process of subdividing the attributes of a relation into groups, creating fragments. It represents an effective way of improving performance in the database systems where a significant percentage of query processing time is spent on the full scans of tables. Most of proposed approaches for vertical partitioning in databases use a pairwise affinity to cluster the attributes of a given relation. The affinity measures the frequency of accessing simultaneously a pair of attributes. The attributes having high affinity are clustered together so as to create fragments containing a maximum of attributes with a strong connectivity. However, such fragments can directly and efficiently be achieved by the use of maximal frequent itemsets. This technique of knowledge engineering reflects better the closeness or affinity when more than two attributes are involved. The partitioning process can be done faster and more accurately with the help of such knowledge discovery technique of data mining. In this paper, an approach based on maximal frequent itemsets to vertical partitioning is proposed to efficiently search for an optimized solution by judiciously pruning the potential search space. Moreover, we propose an analytical cost model to evaluate the produced partitions. Experimental studies show that the cost of the partitioning process can be substantially reduced using only a limited set of potential fragments. They also demonstrate the effectiveness of our approach in partitioning small and large tables

Storage Solutions for Big Data Systems: A Qualitative Study and Comparison

Big data systems development is full of challenges in view of the variety of application areas and domains that this technology promises to serve. Typically, fundamental design decisions involved in big data systems design include choosing appropriate storage and computing infrastructures. In this age of heterogeneous systems that integrate different technologies for optimized solution to a specific real world problem, big data system are not an exception to any such rule. As far as the storage aspect of any big data system is concerned, the primary facet in this regard is a storage infrastructure and NoSQL seems to be the right technology that fulfills its requirements. However, every big data application has variable data characteristics and thus, the corresponding data fits into a different data model. This paper presents feature and use case analysis and comparison of the four main data models namely document oriented, key value, graph and wide column. Moreover, a feature analysis of 80 NoSQL solutions has been provided, elaborating on the criteria and points that a developer must consider while making a possible choice. Typically, big data storage needs to communicate with the execution engine and other processing and visualization technologies to create a comprehensive solution. This brings forth second facet of big data storage, big data file formats, into picture. The second half of the research paper compares the advantages, shortcomings and possible use cases of available big data file formats for Hadoop, which is the foundation for most big data computing technologies. Decentralized storage and blockchain are seen as the next generation of big data storage and its challenges and future prospects have also been discussed

Introducing new constructs for data modelling and column generation in LP modelling languages

Through popular implementation of structured query language (SQL) and query-by-example(QBE) relational databases have become the de-facto industry standard for data modelling.We consider the indices, sets, and the declarative form of Linear Programming (LP) modelling languages and introduce new constructs which provide direct link to the database systems. The models constructed in this way are data driven and display a dynamicstructure. We then show how this approach can be naturally extended to include column generation features stated in procedural forms within an otherwise declarative modelling paradigm