Predictive analytics | EPFL Graph Search

Predictive analytics is a form of business analytics applying machine learning to generate a predictive model for certain business applications. As such, it encompasses a variety of statistical techniques from predictive modeling and machine learning that analyze current and historical facts to make predictions about future or otherwise unknown events. It represents a major subset of machine learning applications; in some contexts, it is synonymous with machine learning. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision-making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU,

Predictive Approach for Database Schema Evolution

Hassina Bounif

Information systems, including their core databases need to meet changing user requirements and adhere to evolving business strategies. Traditional database evolution techniques focus on reacting to change to smoothly perform schema evolution operations and to propagate corresponding updates to the data as effectively as possible. Adopting such posterior solutions to such changes generates high costs in human resources and financial support. We advocate an alternate solution: a predictive approach to database evolution. In this approach, we anticipate future changes during the standard requirements analysis phase of schema development. Our approach enables potential future requirements to be planned for, as well as the standard, determining what data is to be stored and what access is required. This preparation contributes significantly in the ability of the database schema to adapt to future changes and to estimate their relative costs.

Idea Group2006

Predictive approach for schema evolution

Hassina Bounif

In today's companies and organizations, databases are omnipresent. They are the means to represent and store information. Therefore, they must evolve whenever the information architecture (semantic and structure) changes. Databases are subject to evolution for several reasons that include the changes to the real-world or the emergence of new database user requirements. We may also consider non representational and non-functional aspects such as the technology and performance requirements. Those can also compel the database designer or administrator to change the database in order to be more optimal and adapted to those requirements. This thesis proposes to address the schema evolution problem by anticipating the potential changes for the evolution of the schema. The approach adopts the a priori perspective i.e. to plan in advance for a possible solution that makes a schema evolve over time. Such a solution works with both a set of assumptions and techniques. The elaborated approach is based on different components: • First, we propose to develop a schema repository which contains a wide number of heterogeneous schemas and their historical versions. This repository serves in a series of operations that are important for the study of the schemas such as selecting the pertinent schemas, building the change matrices and performing the data-mining process. • Second, we propose to develop a requirements ontology, meaning to say a domain ontology that describes the current concepts of a database as well as the concepts that represent the potential future requirements that could be part of the evolution. The method of construction of this ontology includes several phases such as the preparation of the data for this ontology, the establishment of the data-dictionary besides change perspectives that store the concepts that are associated to other concepts by relationships resulting from the application of the data-mining process, the evaluation of the domain ontology using a graph structure and finally the construction of the ontology using a set of procedures. • Third, we propose to develop a predicted schema which is a database schema that contains two types of metadata. The metadata, that represents current entities of the database and the metadata that represents potential future entities. The predicted schema has two methods of representation: the multi-representation strategy and the predicted design repository schemas strategy. The schemas with the multi-representation strategy are particular versions of schemas whereas the schemas with the predicted design repository schemas are particular repositories of schemas. • Finally, we propose a case study and an analysis of the approach. The case study to better understand how this approach works and the analysis to show its feasibility, the positive as well as negative aspects.

EPFL2009

State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data

Michael Ingram, Mario Paolone, Panagiotis Papadopoulos

Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, a group of experts have worked together to highlight and establish a baseline set of these common concerns, which can be used as motivation to propose innovative analytics and data-driven solutions. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field.

ELSEVIER SCI LTD2022

Predictive approach for schema evolution

Hassina Bounif

EPFL2009