Data stream management and mining

As we have mentioned, we are interested in detecting events, which are emitted when a function, computed over the data of different distributed nodes, has crossed a specific

Motivated by the demand of having algorithms that use lim- ited memory space for discovering collections of frequently co-occurring connected edges from big, interlinked, dynamic

Streaming AP ( StrAP ) is our proposed understandable, stable and computationally efficient data stream clustering method. The online updating clustering model is maintained by

We evaluated these activity profiles using the CrowdFlower Web- based crowdsourcing platform (restricted to Amazon Mechanical Turk workers). Each work unit asks a worker to

Two of these features are (1) continuous location interpolation and near- future prediction and (2) fast moving objects index struc- tures for windows with high fluctuation.. In

Mining Internet of Things (IoT) Big Data streams is a challenging task, that needs new tools to per- form the most common machine learning algo- rithms such as

Real time ana- lytics is becoming the fastest and most efficient way to obtain useful knowledge from what is happening now, allowing organizations to react quickly when prob-

Database and data mining techniques aim at efficiently analyzing these large and complex data to support new insights and decision making based on the extraction of regular or