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Data Stream Mining
Data Stream Mining (also known as stream learning) is the process of extracting knowledge structures from continuous, rapid data records. A data stream is an ordered sequence of instances that in many applications of data stream mining can be read only once or a small number of times using limited computing and storage capabilities. In many data stream mining applications, the goal is to predict the class or value of new instances in the data stream given some knowledge about the class membership or values of previous instances in the data stream. Machine learning techniques can be used to learn this prediction task from labeled examples in an automated fashion. Often, concepts from the field of incremental learning are applied to cope with structural changes, on-line learning and real-time demands. In many applications, especially operating within non-stationary environments, the distribution underlying the instances or the rules underlying their labeling may change over time, i. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Stream
In connection-oriented communication, a data stream is the transmission of a sequence of digitally encoded signals to convey information. Typically, the transmitted symbols are grouped into a series of packets. Data streaming has become ubiquitous. Anything transmitted over the Internet is transmitted as a data stream. Using a mobile phone to have a conversation transmits the sound as a data stream. Formal definition In a formal way, a data stream is any ordered pair ( s, \Delta ) where: # s is a sequence of tuples and # \Delta is a sequence of positive real time intervals. Content Data Stream contains different sets of data, that depend on the chosen data format. * Attributes – each attribute of the data stream represents a certain type of data, e.g. segment / data point ID, timestamp, geodata. * Timestamp attribute helps to identify when an event occurred. * Subject ID is an encoded-by-algorithm ID, that has been extracted out of a cookie. * Raw Data in ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Big Data
Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data processing, data-processing application software, software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate. Big data analysis challenges include Automatic identification and data capture, capturing data, Computer data storage, data storage, data analysis, search, Data sharing, sharing, Data transmission, transfer, Data visualization, visualization, Query language, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: ''volume'', ''variety'', and ''velocity''. The analysis of big data presents challenges in sampling, and thus previously allowing for only observations and sampling. Thus a fourth concept, ''veracity,'' refers to the quality or insightfulness of the data. Without sufficient investm ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Wireless Sensor Network
Wireless sensor networks (WSNs) refer to networks of spatially dispersed and dedicated sensors that monitor and record the physical conditions of the environment and forward the collected data to a central location. WSNs can measure environmental conditions such as temperature, sound, pollution levels, humidity and wind. These are similar to wireless ad hoc networks in the sense that they rely on wireless connectivity and spontaneous formation of networks so that sensor data can be transported wirelessly. WSNs monitor physical conditions, such as temperature, sound, and pressure. Modern networks are bi-directional, both collecting data and enabling control of sensor activity. The development of these networks was motivated by military applications such as battlefield surveillance. Such networks are used in industrial and consumer applications, such as industrial process monitoring and control and machine health monitoring and agriculture. A WSN is built of "nodes" – from a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Stream Processing
In computer science, stream processing (also known as event stream processing, data stream processing, or distributed stream processing) is a programming paradigm which views Stream (computing), streams, or sequences of events in time, as the central input and output objects of computation. Stream processing encompasses dataflow programming, reactive programming, and Distributed computing, distributed data processing. Stream processing systems aim to expose parallel computing, parallel processing for data streams and rely on streaming algorithms for efficient implementation. The Solution stack, software stack for these systems includes components such as programming models and query languages, for expressing computation; data stream management system, stream management systems, for distribution and scheduling (computing), scheduling; and hardware components for hardware acceleration, acceleration including floating-point units, graphics processing units, and field-programmable gate a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Streaming Algorithm
In computer science, streaming algorithms are algorithms for processing data streams in which the input is presented as a sequence of items and can be examined in only a few passes, typically one-pass algorithm, just one. These algorithms are designed to operate with limited memory, generally L (complexity), logarithmic in the size of the stream and/or in the maximum value in the stream, and may also have limited processing time per item. As a result of these constraints, streaming algorithms often produce approximate answers based on a summary or "sketch" of the data stream. History Though streaming algorithms had already been studied by Munro and Paterson as early as 1978, as well as Philippe Flajolet and G. Nigel Martin in 1982/83, the field of streaming algorithms was first formalized and popularized in a 1996 paper by Noga Alon, Yossi Matias, and Mario Szegedy. For this paper, the authors later won the Gödel Prize in 2005 "for their foundational contribution to streaming ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Sequence Mining
Sequential pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity. Sequential pattern mining is a special case of structured data mining. There are several key traditional computational problems addressed within this field. These include building efficient databases and indexes for sequence information, extracting the frequently occurring patterns, comparing sequences for similarity, and recovering missing sequence members. In general, sequence mining problems can be classified as ''string mining'' which is typically based on string processing algorithms and ''itemset mining'' which is typically based on association rule learning. ''Local process models'' extend sequential pattern mining to more complex patterns that can inc ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Data Mining
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data set and transforming the information into a comprehensible structure for further use. Data mining is the analysis step of the " knowledge discovery in databases" process, or KDD. Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term "data mining" is a misnomer because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (''mining'') of data itself. It also is a buzzwo ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Concept Drift
In predictive analytics, data science, machine learning and related fields, concept drift or drift is an evolution of data that invalidates the data model. It happens when the statistical properties of the target variable, which the model is trying to predict, change over time in unforeseen ways. This causes problems because the predictions become less accurate as time passes. Drift detection and drift adaptation are of paramount importance in the fields that involve dynamically changing data and data models. Predictive model decay In machine learning and predictive analytics this drift phenomenon is called concept drift. In machine learning, a common element of a data model are the statistical properties, such as probability distribution of the actual data. If they deviate from the statistical properties of the training data set, then the learned predictions may become invalid, if the drift is not addressed. Data configuration decay Another important area is software engineering, ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Germany
Germany, officially the Federal Republic of Germany, is a country in Central Europe. It lies between the Baltic Sea and the North Sea to the north and the Alps to the south. Its sixteen States of Germany, constituent states have a total population of over 84 million in an area of , making it the most populous member state of the European Union. It borders Denmark to the north, Poland and the Czech Republic to the east, Austria and Switzerland to the south, and France, Luxembourg, Belgium, and the Netherlands to the west. The Capital of Germany, nation's capital and List of cities in Germany by population, most populous city is Berlin and its main financial centre is Frankfurt; the largest urban area is the Ruhr. Settlement in the territory of modern Germany began in the Lower Paleolithic, with various tribes inhabiting it from the Neolithic onward, chiefly the Celts. Various Germanic peoples, Germanic tribes have inhabited the northern parts of modern Germany since classical ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Berlin
Berlin ( ; ) is the Capital of Germany, capital and largest city of Germany, by both area and List of cities in Germany by population, population. With 3.7 million inhabitants, it has the List of cities in the European Union by population within city limits, highest population within its city limits of any city in the European Union. The city is also one of the states of Germany, being the List of German states by area, third smallest state in the country by area. Berlin is surrounded by the state of Brandenburg, and Brandenburg's capital Potsdam is nearby. The urban area of Berlin has a population of over 4.6 million and is therefore the most populous urban area in Germany. The Berlin/Brandenburg Metropolitan Region, Berlin-Brandenburg capital region has around 6.2 million inhabitants and is Germany's second-largest metropolitan region after the Rhine-Ruhr region, as well as the List of EU metropolitan areas by GDP, fifth-biggest metropolitan region by GDP in the European Union. ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Hong Kong
Hong Kong)., Legally Hong Kong, China in international treaties and organizations. is a special administrative region of China. With 7.5 million residents in a territory, Hong Kong is the fourth most densely populated region in the world. Hong Kong was established as a colony of the British Empire after the Qing dynasty ceded Hong Kong Island in 1841–1842 as a consequence of losing the First Opium War. The colony expanded to the Kowloon Peninsula in 1860 and was further extended when the United Kingdom obtained a 99-year lease of the New Territories in 1898. Hong Kong was occupied by Japan from 1941 to 1945 during World War II. The territory was handed over from the United Kingdom to China in 1997. Hong Kong maintains separate governing and economic systems from that of mainland China under the principle of one country, two systems. Originally a sparsely populated area of farming and fishing villages,. the territory is now one of the world's most signific ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Korea
Korea is a peninsular region in East Asia consisting of the Korean Peninsula, Jeju Island, and smaller islands. Since the end of World War II in 1945, it has been politically Division of Korea, divided at or near the 38th parallel north, 38th parallel between North Korea (Democratic People's Republic of Korea; DPRK) and South Korea (Republic of Korea; ROK). Both countries proclaimed independence in 1948, and the two countries fought the Korean War from 1950 to 1953. The region is bordered by China to the north and Russia to the northeast, across the Yalu River, Amnok (Yalu) and Tumen River, Duman (Tumen) rivers, and is separated from Japan to the southeast by the Korea Strait. Known human habitation of the Korean peninsula dates to 40,000 BC. The kingdom of Gojoseon, which according to tradition was founded in 2333 BC, fell to the Han dynasty in 108 BC. It was followed by the Three Kingdoms of Korea, Three Kingdoms period, in which Korea was divided into Goguryeo, Baekje, a ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
