Concept-Oriented Query Language

INTRODUCTION With the explosion of data volume and the variety of data sources (Cohen et al., 2009) – two aspects of the big data problem-we observe quite significant difficulties in applying conventional data analysis methodologies to real world problems. The existing technologies for data management and analytics were pushed to the limits of their ability to solve more and more complex analysis tasks: • Agile Analytics: Perhaps the most widely used methodology for data analysis during several decades is based on the mul-tidimensional metaphor where data is viewed as existing in a multidimensional space. A problem of this approach is that it is based on application-specific scenarios with predefined roles of dimensions, measures, cubes and facts. Changing such scenarios is a quite difficult task because they are embedded in both database systems and client software. The goal of agile analytics consists in going beyond standard OLAP analysis by facilitating exploratory ad-hoc analytics where the user can freely vary all data processing and visualization parameters. • Self-Service Analytics: The conventional approach to analysis is to approach IT department which however has several drawbacks: business frequently does not trust data provided by IT, IT is unable to understand the needs of the user (and this leads to frustration and low motivation), IT might not be able to respond to user requests as quickly as is desirable (and the requirements may well change during the response time), existing BI tools are not intended for non-professional users. Self-service analytics is one of the most significant trends in the BI industry over the last few years and these tools aim to give non-professional users the ability to solve analytical tasks with little or no help from IT. • Near Real Time Analytics: It may take days to generate a BI report in a typical enterprise system and there is strong demand in reducing the time between data acquisition and making a business decision. One of the core problems is that traditional systems are based on two separate technology stacks: for transactional workload and for analytical workload. The design principles and techniques of these two subsystems are quite different and they cannot provide the necessary response time and agility of decision making on large volumes of data (Chaudhuri, Dayal, & Narasayya, 2011; Thiele & Lehner 2012). Although modern hardware provides a basis for a new generation of in-memory, columnar databases (Boncz, 2012; Larson, 2013) with potentially …