On the Continuity of Effective Multifunctions

One of the most important challenges for machine learning community is to develop efficient classifiers which are able to cope with data streams, especially with the presence of the so-called concept drift. This phenomenon is responsible for the change of classification task characteristics, and poses a challenge for the learning model to adapt itself to the current state of the environment. So there is a strong belief that one-class classification is a promising research direction for data stream analysis--it can be used for binary classification without an access to counterexamples, decomposing a multi-class data stream, outlier detection or novel class recognition. This paper reports a novel modification of weighted one-class support vector machine, adapted to the non-stationary streaming data analysis. Our proposition can deal with the gradual concept drift, as the introduced one-class classifier model can adapt its decision boundary to new, incoming data and additionally employs a forgetting mechanism which boosts the ability of the classifier to follow the model changes. In this work, we propose several different strategies for incremental learning and forgetting, and additionally we evaluate them on the basis of several real data streams. Obtained results confirmed the usability of proposed classifier to the problem of data stream classification with the presence of concept drift. Additionally, implemented forgetting mechanism assures the limited memory consumption, because only quite new and valuable examples should be memorized.

In recent years, a significant boost in data availability for persistent data streams has been observed. These data streams are continually evolving, with the clusters frequently forming arbitrary shapes instead of regular shapes in the data space. This characteristic leads to an exponential increase in the processing time of traditional clustering algorithms for data streams. In this study, we propose a new online method, which is a density grid-based method for data stream clustering. The primary objectives of the density grid-based method are to reduce the number of distant function calls and to improve the cluster quality. The method is conducted entirely online and consists of two main phases. The first phase generates the Core Micro-Clusters (CMCs), and the second phase combines the CMCs into macro clusters. The grid-based method was utilized as an outlier buffer in order to handle multi-density data and noises. The method was tested on real and synthetic data streams employing different quality metrics and was compared with the popular method of clustering evolving data streams into arbitrary shapes. The proposed method was demonstrated to be an effective solution for reducing the number of calls to the distance function and improving the cluster quality.

The process of employing a discrete Hilbert transform to derive a complex data stream from a real data stream has been described in the literature but not the converse operation. A technique is provided to construct an equivalent real data set from a complex data set which guarantees by construction that the proper symmetry conditions on the Fourier transform of the data set are met. The technique is also useful for computing the real impulse response associated with a complex transfer function.

Deterministic K-set structure

A uniform approach to computing with infinite objects like real numbers, tuples of these, compacts sets and uniformly continuous maps is presented. In the work of Berger, it was shown how to extract certified algorithms working with the signed digit representation from constructive proofs. Berger and the present author generalised this approach to complete metric spaces and showed how to deal with compact sets. Here, we unify this work and lay the foundations for doing a similar thing for the much more comprehensive class of compact Hausdorff spaces occurring in applications. The approach is of the same computational power as Weihrauch’s Type-Two Theory of Effectivity.

Existing scalable data cleaning approaches have focused on batch data cleaning. However, batch data cleaning is not suitable for streaming big data systems, in which dynamic data is generated continuously. Despite the increasing popularity of stream-processing systems, few stream data cleaning techniques have been proposed so far. In this paper, we bridge this gap by addressing the problem of rule-based stream data cleaning, which sets stringent requirements on latency, rule dynamics and ability to cope with the continuous nature of data streams. We design a system, called Bleach, which achieves real-time violation detection and data repair on a dirty data stream. Bleach relies on efficient, compact and distributed data structures to maintain the necessary state to repair data. Additionally, it supports rule dynamics and uses a cumulative sliding window operation to improve cleaning accuracy. We evaluate a prototype of Bleach using both synthetic and real data streams and experimentally validate its high throughput, low latency and high cleaning accuracy, which are preserved even with rule dynamics.

Many emerging distributed applications require the realtime processing of large amounts of data that are being updated continuously. Distributed stream processing systems offer a scalable and efficient means of in-network processing of such data streams. However, the large scale and the distributed nature of such systems, as well as the fluctuation of their load render it difficult to ensure that distributed stream processing applications meet their Quality of Service demands. We describe a decentralized framework for proactively predicting and alleviating hot-spots in distributed stream processing applications in real-time. We base our hot-spot prediction techniques on statistical forecasting methods, while for hot-spot alleviation we employ a non-disruptive component migration protocol. The experimental evaluation of our techniques, implemented in our Synergy distributed stream processing middleware over PlanetLab, using a real stream processing application operating on real streaming data, demonstrates high prediction accuracy and substantial performance benefits. 1.

The main objective of online anomaly detection is to identify abnormal/unusual behavior such as network intrusions, malware infections, over utilized system resources due to design defects etc from real time data stream. Terrabytes of performance data generated in cloud data centers is a well accepted example of such data stream in real time. In this paper, we propose an online anomaly detection framework using non-parametric statistical technique in cloud data center. In order to determine the accuracy of the proposed work, we experiments it to data collected from RUBis cloud testbed and Yahoo Cloud Serving Benchmark (YCSB). Our experimental results shows the greater accuracy in terms of True Positive Rate (TPR), False Positive Rate (FPR), True Negative Rate (TNR) and False Negative Rate (FNR).

Online semi-supervised active learning ensemble classification for evolving imbalanced data streams

The Handbook of Logic in Computer Science is a multi-volume work covering all major areas of application of logic to theoretical computer science. The Handbook comprises six volumes. Each volume contains five or six chapters, giving an in-depth overview of one of the major topics in the field. It is the result of many years of co-operative effort by some of the most eminent frontline researchers in the area. It will no doubt be the standard reference work in logic and theoretical computer science for years to come - essential reading for all those interested in theoretical computer science and logic. volumes cover the background to the subject in terms of mathematical and computational structures. The authors are chosen on an international basis and are leaders in the fields covered. The Handbook is a closely coordinated work which has been under development for the past five years.

The so-called topos approach provides a radical reformulation of quantum theory. Structurally, quantum theory in the topos formulation is very similar to classical physics. There is a state object \(\underline\sum\), analogous to the state space of a classical system, and a quantity-value object \(\underline{\mathbb{R}^{\leftrightarrow}}\), generalising the real numbers. Physical quantities are maps from the state object to the quantity-value object – hence the ‘values’ of physical quantities are not just real numbers in this formalism. Rather, they are families of real intervals, interpreted as ‘unsharp values’. We will motivate and explain these aspects of the topos approach and show that the structure of the quantity-value object \(\underline{\mathbb{R}^{\leftrightarrow}}\) can be analysed using tools from domain theory, a branch of order theory that originated in theoretical computer science. Moreover, the base category of the topos associated with a quantum system turns out to be a domain if the underlying von Neumann algebra is a matrix algebra. For general algebras, the base category still is a highly structured poset. This gives a connection between the topos approach, noncommutative operator algebras and domain theory. In an outlook, we present some early ideas on how domains may become useful in the search for new models of (quantum) space and space-time.

A Big Data Stream Computing (BDSC) Platform handles real-time data from various applications such as risk management, marketing management and business intelligence. Now a days Internet of Things (IoT) deployment is increasing massively in all the areas. These IoTs engender real-time data for analysis. Existing BDSC is inefficient to handle Real-data stream from IoTs because the data stream from IoTs is unstructured and has inconstant velocity. So, it is challenging to handle such real-time data stream. This work proposes a framework that handles real-time data stream through device control techniques to improve the performance. The frame work includes three layers. First layer deals with Big Data platforms that handles real data streams based on area of importance. Second layer is performance layer which deals with performance issues such as low response time, and energy efficiency. The third layer is meant for Applying developed method on existing BDSC platform. The experimental results have been shown a performance improvement 20%-30% for real time data stream from IoT application.

Temporal specifications, such as those found in multi-agent systems, often include cyclic dependencies. Moreover, there is an increasing need to evaluate such specifications in an online manner, upon streaming data. Consider, for example, the online computation of the normative positions of the agents engaging in an e-commerce protocol. We present a formal computational framework that deals with cyclic dependencies in an efficient way. Moreover, we demonstrate the effectiveness of our framework on large synthetic and real data streams, from the fields of multi-agent systems and composite event recognition.

A task-level adaptive MapReduce framework for real-time streaming data in healthcare applications

Analyzing and predicting the high frequency trading (HFT) financial data stream is very challenging due to the fast arrival times and large amount of the data samples. Aiming at solving this problem, an online evolving fuzzy rule-based prediction model is proposed in this paper. Because this prediction model is based on evolving fuzzy rule-based systems and a novel, simpler form of data density, it can autonomously learn from the live data stream, automatically build/remove its rules and recursively update the parameters. This model responds quickly to all unpredictable sudden changes of financial data and re-adjusts itself to follow the new data pattern. Experimental results show the excellent prediction performance of the proposed approach with real financial data stream regardless of quick shifts of data patterns and frequent appearances of abnormal data samples.