Data stream mining

Summary

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

Official source

https://en.wikipedia.org/wiki/Data_stream_mining

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Big-Data streaming applications are used in several domains such as social media analysis, financial analysis, video annotation, surveillance, medical services and traffic prediction. These applications, running on different types of platforms from mobile devices to servers, are characterized by a highly-variable stochastic input data stream, stringent delay constraints and complex task graphs. Several software and hardware optimization techniques have been proposed to maximize the execution quality and the throughput of these applications and to minimize their energy consumption on many-core platforms. By analyzing the existing techniques, one can observe that most solutions classify as a hardware-based task-specific optimization, or as an operating system scheduler optimization, or yet as a load shedding mechanism at the application level. Each of these categories is limited in scope and can be blind to the nature of the program, the data being processed or the characteristics of the hardware. Big-Data streaming applications, due to their wide range of host hardware and content and dynamically-changing input streams, expose the fragmentation of the optimization techniques and create a clear need for a better approach. In this thesis, I propose a suite of energy-efficient hardware-software co-design techniques to bridge the gap between modern Big-Data streaming applications and existing many-core platforms. I choose to model the task graph of the class of applications I consider here by a direct acyclic graph (DAG). First, at the application layer, I propose a unified DAG monitoring solution to process online the general DAG model of the application and provide a set of relevant information that is leveraged at run time by a connected scheduler. At the operating system layer, I propose three different online scheduling solutions for many-core platforms which leverage the feedback from both the application and the hardware layers. The first scheduler addresses the problem of minimizing the energy consumption and the deadlines miss rates of multimedia applications. It takes advantage of the output of the DAG monitoring solution to adapt the mapping of the tasks of multimedia applications to the hardware according to the detected performance and targeted quality of service. The second and third schedulers address the problem of maximizing the quality and throughput and minimizing the energy consumption of Big-Data stream mining applications with single and multiple streams originating from different sources. To cope with the dynamically-changing Big-Data characteristics, the schedulers integrate machine-learning techniques to learn the environment dynamics and the application requirements and adapt the scheduling policy to the desired quality of service. I show that the proposed schedulers are able to scale the execution of data-mining applications to the system capability even in the presence of concept drift. Last, at the hardware layer, to address existing system architectures limitations and to increase even more the throughput, I propose a novel low-power many-core architecture for modern Big-Data stream mining applications that integrates a novel flexible memory hierarchy able to adapt to the dynamic data-driven nature of the input data stream.

EPFL2015

Social and Sensor Data Fusion in the Cloud

Karl Aberer, Ho Young Jeung, Jonnahtan Eduardo Saltarin De Arco, Surender Reddy Yerva

As mobile cloud computing facilitates a wide spectrum of smart applications, the need for fusing various types of data available in the cloud grows rapidly. In particular, social and sensor data lie at the core in such applications, but typically processed separately. This paper explores the potential of fusing social and sensor data in the cloud, presenting a practice—a travel recommendation system that offers the predicted mood information of people on where and when users wish to travel. The system is built upon a conceptual framework that allows to blend the heterogeneous social and sensor data for integrated analysis, extracting weather-dependant people’s mood information from Twitter and meteorological sensor data streams. In order to handle massively streaming data, the system employs various cloud-serving systems, such as Hadoop, HBase, and GSN.

2012

Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing

David Atienza Alonso, Karim Kanoun, Martino Ruggiero

In the last years the process of examining large amounts of different types of data, or Big-Data, in an effort to uncover hidden patterns or unknown correlations has become a major need in our society. In this context, stream mining applications are now widely used in several domains such as financial analysis, video annotation, surveillance, medical services, traffic prediction, etc. In order to cope with the Big-Data stream input and its high variability, modern stream mining applications implement systems with heterogeneous classifiers and adapt online to its input data stream characteristics variation. Moreover, unlike existing architectures for video processing and compression applications, where the processing units are reconfigurable in terms of parameters and possibly even functions as the input data is changing, in Big-Data stream mining applications the complete computing pipeline is changing, as entirely new classifiers and processing functions are invoked depending on the input stream. As a result, new approaches of reconfigurable hardware platform architectures are needed to handle Big-Data streams. However, hardware solutions that have been proposed so far for stream mining applications either target high performance computing without any power consideration (i.e., limiting their applicability in small-scale computing infrastructures or current embedded systems), or they are simply dedicated to a specific learning algorithm (i.e., limited to run with a single type of classifiers). Therefore, in this paper we propose a novel low-power manycore architecture for stream mining applications that is able to cope with the dynamic data-driven nature of stream mining applications while consuming limited power. Our exploration indicates that this new proposed architecture is able to adapt to different classifiers complexities thanks to its multiple scalable vector processing units and their re-configurability feature at runtime. Moreover, our platform architecture includes a memory hierarchy optimized for Big-Data streaming and implements modern fine-grained power management techniques over all the different types of cores allowing then minimum energy consumption for each type of executed classifier

IEEE Press2014

Low Power and Scalable Many-Core Architecture for Big-Data Stream Computing

David Atienza Alonso, Karim Kanoun, Martino Ruggiero

IEEE Press2014

EPFL2015