Data Analysis

It is a large-scale data set of wireless network coverage for over 22,000 4G base stations in France. This data set is generated by applying VoronoiBoost and official sources of base station deployment. The data covers ten main metropolitan areas in the country, encompassing a variety of dense urban, suburban and rural areas.

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This dataset includes gathering 18-month raw PV data at time intervals of about 200 µs (5 kHz sampling). A post-processing 365-day day-by-day downsampled version, converted to 10 ms intervals (100 Hz sampling), is also included. The end results are two databases: 1. The original, raw, data, including both fast (short circuit, 200 µs) and slow (sweep, 2.5-3.9 s) information for 18 months. These show intervals of missing points, but are provided to allow potential users to reproduce any new work. 2.

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Archival bundle of District Information System for Education (DISE) Delhi primary to upper-primary level schools in academic session 2012-2013. DISE is a school-level dataset consisting of government-recognized schools. It is a joint initiative of the Government of India, UNICEF and the National University of Education and Planning (NUPEA).

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Archival bundle of District Information System for Education (DISE) Delhi primary to upper-primary level schools in academic session 2012-2013. DISE is a school-level dataset consisting of government-recognized schools. It is a joint initiative of the Government of India, UNICEF and the National University of Education and Planning (NUPEA).

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Here we intend to review the current state of the art of interactive data exploration techniques,

and to discover the maturity of the existing solutions. We look for solutions that satisfy three requirements:

access to raw data files, stored in a distributed environment, and with a reasonably low latency.

Method This work follows the well-defined guidelines for Systematic Mapping Studies, which is well-

suited for gathering and classifying available studies.

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