On the Effect of Adaptive and Nonadaptive Analysis of Time-Series Sensory Data

Kolozali, Sefki; Puschmann, Daniel; Bermúdez-Edo, María; Barnaghi, Payam

doi:10.1109/jiot.2016.2553080

Cited by 34 publications

(12 citation statements)

References 34 publications

(36 reference statements)

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“…As normalized time series data assumes a Gaussian distribution for the data, the discretization phase allows to obtain a symbolic representation of the data by mapping the PAA coefficients to a set of equiprobable breakpoints that are produced according to the alphabet size α. The breakpoints determine equalsized areas under the Gaussian curve [31] in which each area is assigned to an alphabet character.…”

Section: Symbolic Aggregate Approximation (Sax)mentioning

confidence: 99%

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

González-Vidal

Barnaghi

Skarmeta

2018

IEEE Trans. Knowl. Data Eng.

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Abstract-The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced mutations of the data (i.e. data drifts). The algorithm splits the data streams into blocks and groups them in square matrices, computes the Discrete Cosine Transform (DCT) and quantizes them. The key information is contained in the upper-left part of the resulting matrix. We extract this sub-matrix, compute the modulus of its eigenvalues and remove duplicates. The algorithm, called BEATS, is designed to tackle dynamic IoT streams, whose distribution changes over time. We implement experiments with six datasets combining real, synthetic, real-world data, and data with drifts. Compared to other segmentation methods like Symbolic Aggregate approXimation (SAX), BEATS shows significant improvements. Trying it with classification and clustering algorithms it provides efficient results. BEATS is an effective mechanism to work with dynamic and multi-variate data, making it suitable for IoT data sources. The datasets, code of the algorithm and the analysis results can be accessed publicly at: https://github.com/auroragonzalez/BEATS.

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Section: Symbolic Aggregate Approximation (Sax)mentioning

confidence: 99%

BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation

González-Vidal

Barnaghi

Skarmeta

2018

IEEE Trans. Knowl. Data Eng.

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“…5 that currently has a semantic model focused on communications, VITAL 6 for smart cities, CityPulse 7 with more focus on data [18] and OpenIoT 8 , which is an extension of SSN. Performance of ontologies for large data sets have been addressed by different methods, such as by redesigning the data storage model and leveraging expected query patterns [27].…”

Section: Related Workmentioning

confidence: 99%

“…We have created this taxonomy using individuals from well-know ontologies, such as qu-rec20 17 and qudt 18 . The spatial dimension of the ontology is addressed with the geo ontology 19 based on WGS84 location coordinates 20 .…”

Section: Iot-lite: Iot Modelling and Semantic Annotationmentioning

confidence: 99%

“…Most applications could work properly with less data, efficiently aggregated. With the same aim as IoT-Lite, the SAO ontology (Stream Annotation Ontology) was created in order to deal with huge amount of IoT data in a efficient manner [18]. The SAO ontology provides annotation means to represent aggregated data in a lightweight ontology [16].…”

Section: Extending Iot-lite With Data Aggregationmentioning

confidence: 99%

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IoT-Lite: a lightweight semantic model for the internet of things and its use with dynamic semantics

Bermúdez-Edo

Elsaleh

Barnaghi

et al. 2017

Pers Ubiquit Comput

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104

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Over the past few years the semantics community has developed several ontologies to describe concepts and relationships for Internet of Things (IoT) applications. A key problem is that most of the IoT related semantic descriptions are not as widely adopted as expected. One of the main concerns of users and developers is that semantic techniques increase the complexity and processing time and therefore they are unsuitable for dynamic and responsive environments such as the IoT. To address this concern, we propose IoT-Lite, an instantiation of the semantic sensor network (SSN) ontology to describe key IoT concepts allowing interoperability and discovery of sensory data in heterogeneous IoT platforms by a lightweight semantics. We propose 10 rules for good and scalable semantic model design and follow them to create IoT-Lite. We also demonstrate the scalability of IoT-Lite by providing some experimental analysis, and assess IoT-Lite against another solution in terms of round trip time (RTT) performance for query-response times. We have linked IoTLite with Stream Annotation Ontology (SAO), to allow queries over stream data annotations and we have also added dynamic semantics in the form of MathML annotations to IoT-Lite. Dynamic semantics allows the annotation of spatio-temporal values, reducing storage requirements and therefore the response time for queries. Dynamic semantics stores mathematical formulas to recover estimated values when actual values are missing.

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“…Taking advantage of the sensor deployments in smart cities several recent studies applied big data to urban infrastructure operations [17], [5], [18], [19], [20]. A great 4 number of studies use sensors in taxis, mostly to optimize the taxi routes [21], [22], but also to study the mobility across the city [23] and its flaws to improve the urban planning [2].…”

mentioning

confidence: 99%