Terabyte

Abstract: Digital world is growing very fast and become more complex in the volume (terabyte to petabyte), variety (structured and un-structured and hybrid), velocity (high speed in growth) in nature. This refers to as ‘Big Data’ that is a global phenomenon. This is typically considered to be a data collection that has grown so large it can’t be effectively managed or exploited using conventional data management tools: e.g., classic relational database management systems (RDBMS) or conventional search engines. To handle this problem, traditional RDBMS are complemented by specifically designed a rich set of alternative DBMS; such as - NoSQL, NewSQL and Search-based systems. This paper motivation is to provide - classification, characteristics and evaluation of NoSQL databases in Big Data Analytics. This report is intended to help users, especially to the organizations to obtain an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes of data.

Abstract: We describe a new formulation of appearance-only SLAM suitable for very large scale navigation. The system navigates in the space of appearance, assigning each new observation to either a new or previously visited location, without reference to metric position. The system is demonstrated performing reliable online appearance mapping and loop closure detection over a 1,000 km trajectory, with mean filter update times of 14 ms. The 1,000 km experiment is more than an order of magnitude larger than any previously reported result. The scalability of the system is achieved by defining a sparse approximation to the FAB-MAP model suitable for implementation using an inverted index. Our formulation of the problem is fully probabilistic and naturally incorporates robustness against perceptual aliasing. The 1,000 km data set comprising almost a terabyte of omni-directional and stereo imagery is available for use, and we hope that it will serve as a benchmark for future systems.

Abstract: Research and development of information access technology for scanned paper documents has been hampered by the lack of public test collections of realistic scope and complexity As part of a project to create a prototype system for search and mining of masses of document images, we are assembling a 15 terabyte dataset to support evaluation of both end-to-end complex document information processing (CDIP) tasks (eg, text retrieval and data mining) as well as component technologies such as optical character recognition (OCR), document structure analysis, signature matching, and authorship attribution

Abstract: Many databases have grown to the point where they cannot fit into the fast memory of even large memory machines, to say nothing of current workstations. If what we want to do is to use these data bases to construct predictions of various characteristics, then since the usual methods require that all data be held in fast memory, various work-arounds have to be used. This paper studies one such class of methods which give accuracy comparable to that which could have been obtained if all data could have been held in core and which are computationally fast. The procedure takes small pieces of the data, grows a predictor on each small piece and then pastes these predictors together. A version is given that scales up to terabyte data sets. The methods are also applicable to on-line learning.