6 research outputs found
Learning event patterns for gesture detection
Usability often plays a key role when software is brought to market, including clearly structured workows, the way of presenting information to the user, and, last but not least, how he interacts with the application. In this context, input devices as 3D cameras or (multi-)touch displays became omnipresent in order to define new intuitive ways of user interaction. State-of-the-art systems tightly couple application logic with separate gesture detection components for supported devices. Hard-coded rules or static models obtained by applying machine learning algorithms on many training
samples are used in order to robustly detect a pre defined set of gesture patterns. If possible at all, it becomes difficcult to extend these sets with new patterns or to modify existing ones difficult for both, application developers and end users. Further, adding gesture support for legacy software
or for additional devices becomes dificult with this hardwired approach. In previous research we demonstrated how the database community can contribute to this challenge by leveraging complex event processing on data streams to express gesture patterns. While this declarative approach
decouples application logic from gesture detection components, its major drawback was the non-intuitive definition of gesture queries. In this paper, we present an approach that is related to density-based clustering in order to find declarative gesture descriptions using only a few samples.
We demonstrate the algorithms on mining definitions for multi-dimensional gestures from the sensor data stream that is delivered by a Microsoft Kinect 3D camera, and provide a way for non-expert users to intuitively customize gesturecontrolled user interfaces even during runtime
Enhancing performance and expressibility of complex event processing using binary tree-based directed graph
In various domains, applications are required to detect and react to complex situations accordingly. In response to the demand for matching receiving events to complex patterns, several event processing systems have been developed. However, there are just a few of them considered both performance and expressibility of event matching as focusing only on performance can cause negative effect on the expressibility or vice versa. This research develops a fast adaptive event matching system (FAEM), a new event matching system to improve expressibility and performance measures (throughput and end-to-end latency). This system is designed and developed based on a novel binary tree-based directed graph (BTDG) as a unified basis for event-matching. The proposed system transforms a user-defined query into a set of system objects including buffers, conditions on buffers, cursors, and join operators (non-kleene and kleene operators) and arranges these objects on a BTDG. Provided BTDG the enhancement in performance of non-kleene operators applied through developing a batch removal method to remove the events that are located out of time-window, and an actual time window (ATW) which can improve performance of event matching. To improve performance of kleene operators, this research introduces a twin algorithms for kleene operator which is match to BTDG. These two kleene algorithms apply grouping on events and reduce the number of intermediate results and apply combination algorithm in final stage. Transformation of queries containing join operators into BTDG enhances the expressibility of the proposed CEP system
Data3–a kinect interface for olap using complex event processing
Abstract—Motion sensing input devices like Microsoft’s Kinect offer an alternative to traditional computer input devices like keyboards and mouses. Daily new applications using this interface appear. Most of them implement their own gesture detection. In our demonstration we show a new approach using the data stream engine AnduIN. The gesture detection is done based on AnduIN’s complex event processing functionality. This way we build a system that allows to define new and complex gestures on the basis of a declarative programming interface. On this basis our demonstration data 3 provides a basic natural interaction OLAP interface for a sample star schema database using Microsoft’s Kinect. I
Change Detection in Streaming Data
Change detection is the process of identifying differences in the state of an object or
phenomenon by observing it at different times or different locations in space. In the
streaming context, it is the process of segmenting a data stream into different segments
by identifying the points where the stream dynamics changes. Decentralized
change detection can be used in many interesting, and important applications such
environmental observing systems, medicare monitoring systems. Although there is
great deal of work on distributed detection and data fusion, most of work focuses
on the one-time change detection solutions. One-time change detection method requires
to proceed data once in response to the change occurring. The trade-off of
a continuous distributed detection of changes include detection accuracy, spaceefficiency,
detection delay, and communication-efficiency.
To achieve these goals, the wildfire warning system is used as a motivating scenario.
From the challenges and requirements of the wildfire warning system, the
change detection algorithms for streaming data are proposed a part of the solution
to the wildfire warning system. By selecting various models of local change detection,
different schemes for distributed change detections, and the data exchange
protocols, different designs can be achieved.
Based on this approach, the contributions of this dissertation are as follows.
A general two-window framework for detecting changes in a single data stream is
presented. A general synopsis-based change detection framework is proposed. Theoretical
and empirical analysis shows that the detection performance of synopsisbased
detector is similar to that of non-synopsis change detector if a distance function
quantifying the changes is preserved under the process of constructing synopsis.
A clustering-based change detection and clustering maintenance method over
sliding window is presented. Clustering-based detector can automatically detect the
changes in the multivariate streaming data. A framework for decentralized change
detection in wireless sensor networks is proposed. A distributed framework for
clustering streaming data is proposed by extending the two-phased stream clustering
approach which is widely used to cluster a single data stream.Unter Ă„nderungserkennung wird der Prozess der Erkennung von Unterschieden im
Zustand eines Objekts oder Phänomens verstanden, wenn dieses zu verschiedenen
Zeitpunkten oder an verschiedenen Orten beobachtet wird. Im Kontext der Datenstromverarbeitung
stellt dieser Prozess die Segmentierung eines Datenstroms anhand
der identifizierten Punkte, an denen sich die Stromdynamiken ändern, dar.
Die Fähigkeit, Änderungen in den Stromdaten zu erkennen, darauf zu reagieren
und sich daran anzupassen, spielt in vielen Anwendungsbereichen, wie z.B.
dem Aktivitätsüberwachung, dem Datenstrom-Mining und Maschinenlernen sowie
dem Datenmanagement hinsichtlich Datenmenge und Datenqualität, eine wichtige
Rolle. Dezentralisierte Ă„nderungserkennung kann in vielen interessanten und
wichtigen Anwendungsbereichen, wie z.B. in UmgebungsĂĽberwachungssystemen
oder medizinischen Ăśberwachungssystemen, eingesetzt werden. Obgleich es eine
Vielzahl von Arbeiten im Bereich der verteilten Ă„nderungserkennung und Datenfusion
gibt, liegt der Fokus dieser Arbeiten meist lediglich auf der Erkennung von
einmaligen Ă„nderungen. Die einmalige Ă„nderungserkennungsmethode erfordert
die einmalige Verarbeitung der Daten als Antwort auf die auftretende Ă„nderung.
Der Kompromiss einer kontinuierlichen, verteilten Erkennung von Ă„nderungen
umfasst die Erkennungsgenauigkeit, die Speichereffizienz sowie die Berechnungseffizienz.
Um dieses Ziel zu erreichen, wird das Flächenbrandwarnsystem
als motivierendes Szenario genutzt. Basierend auf den Herausforderungen und Anforderungen
dieses Warnsystems wird ein Algorithmus zur Erkennung von Ă„nderungen
in Stromdaten als Teil einer Gesamtlösung für das Flächenbrandwarnsystem
vorgestellt. Durch die Auswahl verschiedener Modelle zur lokalen und verteilten
Änderungserkennung sowie verschiedener Datenaustauschprotokolle können
verschiedene Systemdesigns entwickelt werden. Basierend auf diesem Ansatz leistet
diese Dissertation nachfolgend aufgeführte Beiträge. Es wird ein allgemeines
2-Fenster Framework zur Erkennung von Ă„nderungen in einem einzelnen Datenstrom
vorgestellt. Weiterhin wird ein allgemeines synopsenbasiertes Framework
zur Ă„nderungserkennung beschrieben. Mittels theoretischer und empirischer Analysen
wird gezeigt, dass die Erkennungs-Performance des synopsenbasierten Ă„nderungsdetektors
ähnlich der eines nicht-synopsenbasierten ist, solange eine Distanzfunktion,
welche die Änderungen quantifiziert, während der Erstellung der
Synopse eingehalten wird. Es wird Cluster-basierte Ă„nderungserkennung und
Cluster-Pflege ĂĽber gleitenden Fenstern vorgestellt.Weiterhin wird ein Framework
zur verteilten Ă„nderungserkennung in drahtlosen Sensornetzwerken beschrieben.
Basierend auf dem 2-Phasen Stromdaten-Cluster-Ansatz, welcher weitestgehend
zur Clusterung eines einzelnen Datenstroms eingesetzt wird, wird ein verteiltes
Framework zur Clusterung von Stromdaten vorgestellt
A Model-Based Approach for Gesture Interfaces
The description of a gesture requires temporal analysis of values generated by input sensors, and it does not fit well the observer pattern traditionally used by frameworks to handle the user’s input. The current solution is to embed particular gesture-based interactions into frameworks by notifying when a gesture is detected completely. This approach suffers from a lack of flexibility, unless the programmer performs explicit temporal analysis of raw sensors data.
This thesis proposes a compositional, declarative meta-model for gestures definition based on Petri Nets. Basic traits are used as building blocks for defining gestures; each one notifies the change of a feature value. A complex gesture is defined by the composition of other sub-gestures using a set of operators. The user interface behaviour can be associated to the recognition of the whole gesture or to any other sub-component, addressing the problem of granularity for the notification of events.
The meta-model can be instantiated for different gesture recognition supports and its definition has been validated through a proof of concept library. Sample applications have been developed for supporting multi-touch gestures in iOS and full body gestures with Microsoft Kinect.
In addition to the solution for the event granularity problem, this thesis discusses how to separate the definition of the gesture from the user interface behaviour using the proposed compositional approach.
The gesture description meta-model has been integrated into MARIA, a model-based user interface description language, extending it with the description of full-body gesture interfaces