Information Abstraction for Heterogeneous Real World Internet Data

Ganz, Frieder; Barnaghi, Payam; Carrez, François

doi:10.1109/jsen.2013.2271562

Cited by 43 publications

(24 citation statements)

References 18 publications

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“…Nevertheless, the current research approaches only pick certain components to fulfill the goal in their application domain. This leads to the issue of having only few domain-independent approaches to process the IoT [19,35]. With respect to the Big Data issues and the large heterogeneous volume of data that has to be processed, the domain-dependent approaches are not suitable solutions for the problem.…”

Section: Discussionmentioning

confidence: 99%

“…However, the connection between abstraction and observation is created and maintained in the system. Information Abstraction for Heterogeneous Real World Internet Data In our recent work [5] and [19], we extend Henson et al work with a method to model the graph in an automated manner using probabilistic graph modelling techniques and machine-learning methods. Our proposed method finds the significant measurement data and autonomously generate a PCT graph linking observations and abstractions.…”

Section: Semantic Event Processing In Envisionmentioning

confidence: 99%

See 1 more Smart Citation

A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things

Ganz

Puschmann

Barnaghi

et al. 2015

IEEE Internet Things J.

Self Cite

127

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The term Internet of Things (IoT) refers to the interaction and communication between billions of devices that produce and exchange data related to real world objects (i.e. Things). Extracting higher-level information from the raw sensory data captured by the devices and representing this data as machine-interpretable or human-understandable information has several interesting applications. Deriving raw data into higher-level information representations demands mechanisms to find, extract and characterise meaningful abstractions from the raw data. This meaningful abstractions then have to be presented in a human and/or machineunderstandable representation. However, the heterogeneity of the data originated from different sensor devices and application scenarios such as e-health, environmental monitoring and smart home applications and the dynamic nature of sensor data make it difficult to apply only one particular information processing technique to the underlying data. A considerable amount of methods from machine-learning, the semantic web, as well as pattern and data mining have been used to abstract from sensor observations to information representations. This paper provides a survey of the requirements and solutions and describes challenges in the area of information abstraction and presents an efficient workflow to extract meaningful information from raw sensor data based on the current state-of-the-art in this area. The paper also identifies research directions at the edge of information abstraction for sensor data. To ease the understanding of the abstraction workflow process, we introduce a software toolkit that implements the introduced techniques and motivates to apply them on various data sets.

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Section: Discussionmentioning

confidence: 99%

Section: Semantic Event Processing In Envisionmentioning

confidence: 99%

A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things

Ganz

Puschmann

Barnaghi

et al. 2015

IEEE Internet Things J.

Self Cite

127

View full text Add to dashboard Cite

show abstract

“…In this work we use an extended version of the SAX algorithm, called SensorSAX. SensorSAX is optimised for sensor data and is described in our earlier work presented in [10]. SensorSAX exploits a variable encoding rate instead of a constant rate based on the activity in the streaming data and allows higher compression The normalised curve is divided into 9 windows.…”

Section: A Real World Datamentioning

confidence: 99%

“…SAX transforms timeseries data into aggregated words that can be used for pattern detection and indexing. However SAX was not developed for small constrained devices and we therefore introduce Sensor-SAX [10], a modified version that has less data transmission in times of low activity in the sensor signal that is processed. In order to group similar types of patterns and events, clustering mechanisms are used.…”

Section: B Data Processing Mechanismsmentioning

confidence: 99%

Automated Semantic Knowledge Acquisition From Sensor Data

Ganz

Barnaghi

Carrez

2016

IEEE Systems Journal

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Abstract-The gathering of real world data is facilitated by many pervasive data sources such as sensor devices and smart phones. The abundance of the sensory data raise the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organise the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real world data description ontologies and data description models, however little effort has been done in creating and constructing meaningful topical ontologies from vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solutions that construct the structure and relations of ontologies based on Real World Data. To address this challenge we introduce a knowledge acquisition method that processes Real World Data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended k-means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors.

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“…Piecewise Aggregation [27] divides the original data of length N into n equally sized windows by taking the mean of each window. This results in a reduction of data size from N to N/n data points.…”

Section: I) Dimensionality Reduction Phasementioning

confidence: 99%

Design and Implementation of the Sense Egypt Platform for Real-Time Analysis of IoT Data Streams

Rozik¹,

Tolba²,

El-Dosuky³

2016

AIT

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Nowadays, we experience an abundance of Internet of Things middleware solutions that make the sensors and the actuators are able to connect to the Internet. These solutions, referred to as platforms to gain a widespread adoption, have to meet the expectations of different players in the IoT ecosystem, including devices [1]. Low cost devices are easily able to connect wirelessly to the Internet, from handhelds to coffee machines, also known as Internet of Things (IoT). This research describes the methodology and the development process of creating an IoT platform. This paper also presents the architecture and implementation for the IoT platform. The goal of this research is to develop an analytics engine which can gather sensor data from different devices and provide the ability to gain meaningful information from IoT data and act on it using machine learning algorithms. The proposed system is introducing the use of a messaging system to improve the overall system performance as well as provide easy scalability.

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Information Abstraction for Heterogeneous Real World Internet Data

Cited by 43 publications

References 18 publications

A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things

A Practical Evaluation of Information Processing and Abstraction Techniques for the Internet of Things

Automated Semantic Knowledge Acquisition From Sensor Data

Design and Implementation of the Sense Egypt Platform for Real-Time Analysis of IoT Data Streams

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