Using Empirical Recurrence Rates Ratio For Time Series Data Similarity

Several methods exist in classification literature to quantify the similarity between two time series data sets. Applications of these methods range from the traditional Euclidean type metric to the more advanced Dynamic Time Warping metric. Most of these adequately address structural similarity but fail in meeting goals outside it. For example, a tool that could be excellent to identify the seasonal similarity between two time series vectors might prove inadequate in the presence of outliers. In this paper, we have proposed a unifying measure for binary classification that performed well while embracing several aspects of dissimilarity. This statistic is gaining prominence in various fields, such as geology and finance, and is crucial in time series database formation and clustering studies

A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

Privacy-preserving distributed data mining

This thesis is concerned with privacy-preserving distributed data mining algorithms. The main challenges in this setting are inference attacks and the formation of collusion groups. The inference problem is the reconstruction of sensitive data by attackers from non-sensitive sources, such as intermediate results, exchanged messages, or public information. Moreover, in a distributed scenario, malicious insiders can organize collusion groups to deploy more effective inference attacks. This thesis shows that existing privacy measures do not adequately protect privacy against inference and collusion. Therefore, in this thesis, new measures based on information theory are developed to overcome the identiffied limitations. Furthermore, a new distributed data clustering algorithm is presented. The clustering approach is based on a kernel density estimates approximation that generates a controlled amount of ambiguity in the density estimates and provides privacy to original data. Besides, this thesis also introduces the first privacy-preserving algorithms for frequent pattern discovery in a distributed time series. Time series are transformed into a set of n-dimensional data points and finding frequent patterns reduced to finding local maxima in the n-dimensional density space. The proposed algorithms are linear in the size of the dataset with low communication costs, validated by experimental evaluation using different datasets.Diese Arbeit befasst sich mit vertraulichkeitsbewahrendem Data Mining in verteilten Umgebungen mit Schwerpunkt auf ausgewählten N-Agenten-Angriffsszenarien für das Inferenzproblem im Data-Clustering und der Zeitreihenanalyse. Dabei handelt es sich um Angriffe von einzelnen oder Teilgruppen von Agenten innerhalb einer verteilten Data Mining-Gruppe oder von einem einzelnen Agenten außerhalb dieser Gruppe. Zunächst werden in dieser Arbeit zwei neue Privacy-Maße vorgestellt, die im Gegensatz zu bislang existierenden, die im verteilten Data Mining allgemein geforderte Eigenschaften zur Vertraulichkeitsbewahrung erfüllen und bei denen sich der gemessene Grad der Vertraulichkeit auf die verwendete Datenanalysemethode und die Anzahl von Angreifern bezieht. Für den Zweck eines vertraulichkeitsbewahrenden, verteilten Data-Clustering wird ein neues Kernel-Dichteabschätzungsbasiertes Verfahren namens KDECS vorgestellt. KDECS verwendet eine Approximation der originalen, lokalen Kernel-Dichteschätzung, so dass die ursprünglichen Daten anderer Agenten in der Data Mining-Gruppe mit einer höheren Wahrscheinlichkeit als einem hierfür vorgegebenen Wert nicht mehr zu rekonstruieren sind. Das Verfahren ist nachweislich sicherer als Data-Clustering mit generativen Mixture Modellen und SMC-basiert sicherem k-means Data-Clustering. Zusätzlich stellen wir neue Verfahren, namens DPD-TS, DPD-HE und DPDFS, für eine vertraulichkeitsbewahrende, verteilte Mustererkennung in Zeitreihen vor, deren Komplexität und Sicherheitsgrad wir mit den zuvor erwähnten neuen Privacy-Maßen analysieren. Dabei hängt ein von einzelnen Agenten einer Data Mining-Gruppe jeweils vorgegebener, minimaler Sicherheitsgrad von DPD-TS und DPD-FS nur von der Dimensionsreduktion der Zeitreihenwerte und ihrer Diskretisierung ab und kann leicht überprüft werden. Einen noch besseren Schutz von sensiblen Daten bietet das Verfahren DPD HE mit Hilfe von homomorpher Verschlüsselung. Neben der theoretischen Analyse wurden die experimentellen Leistungsbewertungen der entwickelten Verfahren mit verschiedenen, öffentlich verfügbaren Datensätzen durchgeführt

Data Mining and Machine Learning in Astronomy

We review the current state of data mining and machine learning in astronomy. 'Data Mining' can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black-box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those where data mining techniques directly resulted in improved science, and important current and future directions, including probability density functions, parallel algorithms, petascale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm, and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra figures, some minor additions to the tex

Quantum mechanics without quanta

In this paper, I argue that light is a continuous classical electromagnetic wave, while the observed so-called quantum nature of the interaction of light with matter is connected to the discrete (atomic) structure of matter and to the specific nature of the light-atom interaction. From this point of view, the Born rule for light is derived, and the double-slit experiment is analysed in detail. I show that the double-slit experiment can be explained without using the concept of a "photon", solely on the basis of classical electrodynamics. I show that within this framework, the Heisenberg uncertainty principle for a "photon" has a simple physical meaning not related to the fundamental limitations in accuracy of the simultaneous measurement of position and momentum or time and energy. I argue also that we can avoid the paradoxes connected with the wave-particle duality of the electron if we consider some classical wave field - an "electron wave" - instead of electrons as the particles and consider the wave equations (Dirac, Klein-Gordon, Pauli and Schrodinger) as the field equations similar to Maxwell equations for the electromagnetic field. It is shown that such an electron field must have an electric charge, an intrinsic angular momentum and an intrinsic magnetic moment continuously distributed in the space. It is shown that from this perspective, the double-slit experiment for "electrons", the Born rule, the Heisenberg uncertainty principle and the Compton effect all have a simple explanation within classical field theory. The proposed perspective allows consideration of quantum mechanics not as a theory of particles but as a classical field theory similar to Maxwell electrodynamics.Comment: 61 pages, 3 figures, Quantum Studies: Mathematics and Foundations, 201

Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure