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Topic Review
Accurate and Invertible Sketch for Super Spread Detection
Super spread detection has been widely applied in network management, recommender systems, and cyberspace security. It is more complicated than heavy hitter owing to the requirement of duplicate removal. Accurately detecting a super spread in real-time with small memory demands remains a nontrivial yet challenging issue. 
  • 91
  • 11 Jan 2024
Topic Review
Heart Rate Variability towards Noninvasive Glucose Measurement
Heart rate variability (HRV) is defined by the heart rate variations caused by the periodic change of heart rhythm over time in the absence of physiological activity, postural changes, and emotional stimuli. This labels HRV as a noninvasive marker of the autonomic nervous system (ANS) function. Heart rate variability (HRV) parameters can reveal the performance of the autonomic nervous system and possibly estimate the type of its malfunction, such as that of detecting the blood glucose level. 
  • 139
  • 14 Nov 2023
Topic Review
Consensus Big Data Clustering for Bayesian Mixture Models
In the context of big-data analysis, the clustering technique holds significant importance for the effective categorization and organization of extensive datasets. However, pinpointing the ideal number of clusters and handling high-dimensional data can be challenging. To tackle these issues, several strategies have been suggested, such as a consensus clustering ensemble that yields more significant outcomes compared to individual models. Another valuable technique for cluster analysis is Bayesian mixture modelling, which is known for its adaptability in determining cluster numbers. 
  • 166
  • 18 Aug 2023
Topic Review
Six Stages to Choose Sampling Techniques
In order to answer the research questions, it is doubtful that researcher should be able to collect data from all cases. Thus, there is a need to select a sample. The entire set of cases from which researcher sample is drawn in called the population. Since, researchers neither have time nor the resources to analysis the entire population so they apply sampling technique to reduce the number of cases. There are six stages to choose sampling techniques.
  • 94.3K
  • 17 Jul 2023
Topic Review Peer Reviewed
Application of Mobile Operators’ Data in Modern Geographical Research
Mobile operators’ data are one type of Big Data. These are any data about events related to the use of a mobile phone. These data include subscriber identifiers and associated time and location attributes. Big Data in general usually includes datasets with sizes beyond the ability of commonly used software tools to capture, curate, manage, and process data within a tolerable elapsed time. Big Data can be described by the following key characteristics: volume, variety, velocity, veracity, value, variability etc. Mobile operators’ data are supplied by the Mobile Network Operators. The main distinguishing features of the operator are, firstly, the possession of a state license to use the radio frequency spectrum, and, secondly, the possession or control over the elements of the network infrastructure necessary to provide services to subscribers in the authorized radio frequency spectrum. The smallest structural territorial element for cellular communication systems is a cell; its dimensions can be different (250 by 250 m, 500 by 500 m, etc.).
  • 307
  • 01 Dec 2022
Topic Review
SIGKDD
SIGKDD is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining. It became an official ACM SIG in 1998.
  • 380
  • 18 Nov 2022
Topic Review
Data Mining in Agriculture
Data mining in agriculture is a recent research topic, consisting of the application of data mining techniques to agriculture. Recent technologies are able to provide extensive information on agricultural-related activities, which can then be analyzed in order to find relevant information. A related, but not equivalent term is precision agriculture.
  • 720
  • 18 Nov 2022
Topic Review
Data Analysis/4 Data Mining
Data mining (sometimes called knowledge discovery) is the process of analyzing and summarizing data into useful information which can be used to understand common features, the origin of data and to extract hidden predictive information. Data mining is used in science, engineering, modeling and analysis of financial markets.
  • 375
  • 15 Nov 2022
Topic Review
Sports Analytics
Sports analytics are a collection of relevant, historical, statistics that can provide a competitive advantage to a team or individual. Through the collection and analyzation of these data, sports analytics inform players, coaches and other staff in order to facilitate decision making both during and prior to sporting events. The term "sports analytics" was popularized in mainstream sports culture following the release of the 2011 film, Moneyball, in which Oakland Athletics General Manager Billy Beane (played by Brad Pitt) relies heavily on the use of analytics to build a competitive team on a minimal budget. There are two key aspects of sports analytics — on-field and off-field analytics. On-field analytics deals with improving the on-field performance of teams and players, including questions such as "which player on the Red Sox contributed most to the team's offense?" or "who is the best wing player in the NBA?", etc. Off-field analytics deals with the business side of sports. Off-field analytics focuses on helping a sport organization or body surface patterns and insights through data that would help increase ticket and merchandise sales, improve fan engagement, etc. Off-field analytics essentially uses data to help rightsholders take decisions that would lead to higher growth and increased profitability. As technology has advanced over the last number of years data collection has become more in-depth and can be conducted with relative ease. Advancements in data collection have allowed for sports analytics to grow as well, leading to the development of advanced statistics and machine learning, as well as sport specific technologies that allow for things like game simulations to be conducted by teams prior to play, improve fan acquisition and marketing strategies, and even understand the impact of sponsorship on each team as well as its fans. Another significant impact sports analytics have had on professional sports is in relation to sport gambling. In depth sports analytics have taken sports gambling to new levels, whether it be fantasy sports leagues or nightly wagers, bettors now have more information at their disposal to help aid decision making. A number of companies and webpages have been developed to help provide fans with up to the minute information for their betting needs.
  • 898
  • 14 Nov 2022
Topic Review
Homoscedasticity
In statistics, a sequence (or a vector) of random variables is homoscedastic/ˌhoʊmoʊskəˈdæstɪk/ if all its random variables have the same finite variance. This is also known as homogeneity of variance. The complementary notion is called heteroscedasticity. The spellings homoskedasticity and heteroskedasticity are also frequently used. Assuming a variable is homoscedastic when in reality it is heteroscedastic (/ˌhɛtəroʊskəˈdæstɪk/) results in unbiased but inefficient point estimates and in biased estimates of standard errors, and may result in overestimating the goodness of fit as measured by the Pearson coefficient.
  • 790
  • 31 Oct 2022
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