Lambda architecture

Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream-processing methods. This approach to architecture attempts to balance latency , throughput , and fault-tolerance by using a combination of real-time data processing and data processing. The two view outputs may be joined before presentation. The rise of lambda architecture is correlated with the growth of big data , real-time analytics, and the drive to mitigate the latencies of map-reduce. [1] Continue reading “Lambda architecture”

IT operations analytics

In the fields of information technology (IT) and systems management , IT operations (ITOA) is an approach or method to retrieve, analyze, and report data for IT operations. ITOA may apply big data analytics to large datasets to produce business insights. [1] [2] In 2014, Gartner predicted its use to increase revenue or reduce costs. [3] By 2017, it is predicted that 15% of enterprises will use IT operations analytics technologies. [2] Continue reading “IT operations analytics”

Intelligence engine

An intelligence engine is a type of enterprise information management that combines business rule management , predictive , and prescriptive analytics to form a unified information-access platform that provides real-time intelligence through search technologies , dashboards and / or existing business infrastructure. Intelligence Engines are process and / or business problem specific, resulting in industry and / or function-specific marketing. They can be differentiated from enterprise resource planning (ERP)decision management functionality. Continue reading “Intelligence engine”

Industrial big data

Industrial big data refers to a large amount of diversified time series generated at a high speed by industrial equipment,[1] known as the Internet of things[2]The term emerged in 2012 along with the concept of “Industry 4.0”, and refers to big data”, popular in information technology marketing, in that data created by industrial equipment might hold more potential business values.[3] Industrial big data takes advantage of industrial Internet technology. It uses raw data to support management decision making, so to reduce costs in maintenance and improve customer service.[2] Continue reading “Industrial big data”

Head / tail Breaks

Head / tail breaks is a clustering algorithm with heavy-tailed distributions such as power laws and lognormal distributions . The heavy-tailed distribution can be simply referred to the scaling pattern of large, small, or small, largest and smallest. The classification is done through a large part of the world (or called the head) and small (or called the tail). Arithmetic mean or average, and then recursively going for the division process. of far more small things than large ones [1]Head / tail breaks is not just for classification, but also for visualization of big data by keeping the head, since the head is self-similar to the whole. Head / tail breaks can be applied not only to vector data such as points, lines and polygons, but also to raster data like the digital elevation model (DEM). Continue reading “Head / tail Breaks”

GIS United

GIS United (GU / GIS Utd) is a union of GIS specialists who have a variety of backgrounds such as business administration, public administration, environmental engineering, mechanical engineering, statistics, urban engineering, architecture, historical studies, literature, art, etc. As a consulting firm to analyze Geo-spatial Big data specializes headquartered in Mapo Seogyo, Seoul, South Korea . Continue reading “GIS United”

Flutura Decision Sciences and Analytics

Flutura Decision Sciences and Analytics is an industrial Internet of things (IoT) company that focuses on machine to machine and big data analytics serving customers from manufacturing, energy and engineering industries. Its main offices are located in Palo Alto, California and has its development center in Bengaluru, India. Continue reading “Flutura Decision Sciences and Analytics”


Dataveillance is the practice of monitoring and collecting metadata. [1] The word is a portmanteau of data and surveillance. [2] Dataveillance is concerned with the continuous monitoring of users’ communications and actions across various platforms. [3] For instance, dataveillance refers to the monitoring of data resulting from credit card transactions, GPS coordinates, emails, social networks , etc. Using digital media often leaves traces of data and creates a digital footprint of our activity. [4] This type of surveillance is not often known and is inconsistent. [5]Unlikesubversivity , where individuals willingly monitoring their activity, dataveillance is more discrete and unknown. Dataveillance may involve the monitoring of groups of individuals. There exist three types of dataveillance: personal dataveillance, mass dataveillance, and facilitiative mechanisms . [3] Continue reading “dataveillance”