Combined PIM-PSM

The Model Driven Architecture (MDA) defines an alternative approach to software development. It allows to separate the system functionality specification from its implementation on any specific technology platform. Following the MDA approach, the first step of a software development project is the creation of a Platform Independent Model (PIM). Such PIM can then be mapped to one or more Platform Specific Models (PSMs). Keeping a PIM and its PSMs separate has many advantages, but it also leads to some problems. One of them is the synchronization of several models. Whenever a PIM is updated, all corresponding PSMs must also be updated to reflect the changes. A solution could be to combine a PIM and all its PSM in one and the same model. How this can be done is the main question of this thesis

The Family of MapReduce and Large Scale Data Processing Systems

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large scale data processing mechanisms. MapReduce is a simple and powerful programming model that enables easy development of scalable parallel applications to process vast amounts of data on large clusters of commodity machines. It isolates the application from the details of running a distributed program such as issues on data distribution, scheduling and fault tolerance. However, the original implementation of the MapReduce framework had some limitations that have been tackled by many research efforts in several followup works after its introduction. This article provides a comprehensive survey for a family of approaches and mechanisms of large scale data processing mechanisms that have been implemented based on the original idea of the MapReduce framework and are currently gaining a lot of momentum in both research and industrial communities. We also cover a set of introduced systems that have been implemented to provide declarative programming interfaces on top of the MapReduce framework. In addition, we review several large scale data processing systems that resemble some of the ideas of the MapReduce framework for different purposes and application scenarios. Finally, we discuss some of the future research directions for implementing the next generation of MapReduce-like solutions.Comment: arXiv admin note: text overlap with arXiv:1105.4252 by other author

Transformations of Check Constraint PIM Specifications

Platform independent modeling of information systems and generation of their prototypes play an important role in software development process. However, not all tasks in this process have been covered yet, i.e. not all pieces of an information system can be designed using platform independent artifacts that are later transformable into the executable code. One of the examples is modeling of database check constraints, for which there is a lack of appropriate mechanisms to formally specify them on a platform independent level. In order to provide formal specification of check constraints at platform independent level, we developed a domain specific language and embedded it into a tool for platform independent design and automated prototyping of information systems, named Integrated Information Systems CASE (IIS*Case). In this paper, we present algorithms for transformation of check constraints specified at the platform independent level into the relational data model, and further transformation into the executable SQL/DDL code for several standard and commercial platforms: ANSI SQL-2003, Oracle 9i and 10g, and MS SQL Server 2000 and 2008. We have also implemented these algorithms in IIS*Case as a part of the process of generation of relational database schema

Transparent Forecasting Strategies in Database Management Systems

Whereas traditional data warehouse systems assume that data is complete or has been carefully preprocessed, increasingly more data is imprecise, incomplete, and inconsistent. This is especially true in the context of big data, where massive amount of data arrives continuously in real-time from vast data sources. Nevertheless, modern data analysis involves sophisticated statistical algorithm that go well beyond traditional BI and, additionally, is increasingly performed by non-expert users. Both trends require transparent data mining techniques that efficiently handle missing data and present a complete view of the database to the user. Time series forecasting estimates future, not yet available, data of a time series and represents one way of dealing with missing data. Moreover, it enables queries that retrieve a view of the database at any point in time - past, present, and future. This article presents an overview of forecasting techniques in database management systems. After discussing possible application areas for time series forecasting, we give a short mathematical background of the main forecasting concepts. We then outline various general strategies of integrating time series forecasting inside a database and discuss some individual techniques from the database community. We conclude this article by introducing a novel forecasting-enabled database management architecture that natively and transparently integrates forecast models

State-of-the-art on evolution and reactivity

This report starts by, in Chapter 1, outlining aspects of querying and updating resources on the Web and on the Semantic Web, including the development of query and update languages to be carried out within the Rewerse project. From this outline, it becomes clear that several existing research areas and topics are of interest for this work in Rewerse. In the remainder of this report we further present state of the art surveys in a selection of such areas and topics. More precisely: in Chapter 2 we give an overview of logics for reasoning about state change and updates; Chapter 3 is devoted to briefly describing existing update languages for the Web, and also for updating logic programs; in Chapter 4 event-condition-action rules, both in the context of active database systems and in the context of semistructured data, are surveyed; in Chapter 5 we give an overview of some relevant rule-based agents frameworks

Automated Academic Planning System

Planning and Scheduling of classes in an institute is of pivotal importance. Classes which have to be rescheduled must be immediately notied to the students so that they do not miss the classes and later blame the authorities for not notifying them.Same is the case for classes which are cancelled. Assignments given by faculties must be immediately relayed to the students since the students have to plan their everyday schedule according to the academic workload. Many a times classes are cancelled or rescheduled on a short notice or extra classes are scheduled prior to any information. This leads to students missing a number of classes or wasting their time and energy to attend a class which was already cancelled. Assignments on the other hand fail to get submitted on time due to misinformation by the intermediaries. The general method used to relay such information is taking the help of the class representative of the class, who either sends SMS or emails to every student,which have an history of failing. All the various information about the timing of classes cancelled or rescheduled, submission of assignments and other important notices is handled by one human which increases the chances of failure from time to time.If the class representative is not available then the problem aggravates to a higher level. In this project, we have tried to replace the manual labour involved in the aforementioned issues and created an automated system which eliminates the work of the class representative to some extent. Also a scheduler is made for students to schedule their daily activities along with the classes so that any change in the academic schedule or any pending assignments or other issues will be re ected and they can plan their day accordingly

Ontop: answering SPARQL queries over relational databases

We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases