Stream Reasoning for the Internet of Things

Su, Xiang; Gilman, Ekaterina; Wetz, Peter; Riekki, Jukka; Zuo, Yifei; Leppänen, Teemu

doi:10.1145/2912845.2912853

Cited by 15 publications

(7 citation statements)

References 32 publications

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“…Redis' in-memory data store makes it extremely fast although this implies that available memory size determines the size of the Redis data store [54]. While C-SPARQL and CQELS are excellent for combining static and streaming data, they are not suitable when scalability is required [55]. SAMOA is suitable for machine learning paradigm as it focuses on speed/real-time analytics, scales horizontally and is loosely coupled with its underlying distributed computation platform [56].…”

Section: Research Question 4: What Are the Limitations And Strengths mentioning

confidence: 99%

Big data stream analysis: a systematic literature review

2019

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Advances in information technology have facilitated large volume, high-velocity of data, and the ability to store data continuously leading to several computational challenges. Due to the nature of big data in terms of volume, velocity, variety, variability, veracity, volatility, and value [1] that are being generated recently, big data computing is a new trend for future computing. Big data computing can be generally categorized into two types based on the processing requirements, which are big data batch computing and big data stream computing Abstract Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity. This made it difficult for existing data mining tools, technologies, methods, and techniques to be applied directly on big data streams due to the inherent dynamic characteristics of big data. In this paper, a systematic review of big data streams analysis which employed a rigorous and methodical approach to look at the trends of big data stream tools and technologies as well as methods and techniques employed in analysing big data streams. It provides a global view of big data stream tools and technologies and its comparisons. Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. Out of the initial 2295 papers that resulted from the first search string, 47 papers were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. The study found that scalability, privacy and load balancing issues as well as empirical analysis of big data streams and technologies are still open for further research efforts. We also found that although, significant research efforts have been directed to real-time analysis of big data stream not much attention has been given to the preprocessing stage of big data streams. Only a few big data streaming tools and technologies can do all of the batch, streaming, and iterative jobs; there seems to be no big data tool and technology that offers all the key features required for now and standard benchmark dataset for big data streaming analytics has not been widely adopted. In conclusion, it was recommended that research efforts should be geared towards developing scalable frameworks and algorithms that will accommodate data stream computing mode, effective resource allocation strategy and parallelization issues to cope with the ever-growing size and complexity of data.

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Section: Research Question 4: What Are the Limitations And Strengths mentioning

confidence: 99%

Big data stream analysis: a systematic literature review

2019

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“…Reasoning tasks can be distributed among heterogeneous devices: some are powerful computers, some are edge devices with various constraints. One challenge is to develop computationally-efficient reasoning strategies coping with such heterogeneities [62], and as close to the data sources as possible. One such approach is HyLAR that deploys incremental reasoning tasks on both, the server and the client [64].…”

Section: How To Distribute Reasoning Tasks Among Devicesmentioning

confidence: 99%

Semantics in the Edge: Sensors and actuators in the Web of Linked Data and Things

Cena

Haller

Lefrançois

2020

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“…To bridge this gap, stream reasoning focuses on the scalable and efficient adoption of Semantic Web technologies for streaming data [9]. In the past years, several RDF Stream Processing (RSP) engines have been developed [23], of which C-SPARQL [3] and CQELS [16] are the most well-known. They define a window on top of the stream and allow the registration of semantic queries which are continuously evaluated as data flows through the window.…”

Section: Related Work 21 Stream Reasoningmentioning

confidence: 99%

Personalized Real-Time Monitoring of Amateur Cyclists on Low-End Devices

Brouwer

Ongenae

Daneels

et al. 2018

Companion of the the Web Conference 2018 on the Web Conference 2018 - WWW '18

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Enabling real-time collection and analysis of cyclist sensor data could allow amateur cyclists to continuously monitor themselves, receive personalized feedback on their performance, and communicate with each other during cycling events. Semantic Web technologies enable intelligent consolidation of all available context and sensor data. Stream reasoning techniques allow to perform advanced processing tasks by correlating the consolidated data to enable personalized and context-aware real-time feedback. In this paper, these technologies are leveraged and evaluated to design a Proof-of-Concept application of a personalized real-time feedback platform for amateur cyclists. Real-time feedback about the user's heart rate and heart rate training zones is given through a web application. The performance and scalability of the platform is evaluated on a Raspberry Pi. This shows the potential of the framework to be used in real-life cycling by small groups of amateur cyclists, who can only access low-end devices during events and training. CCS CONCEPTS• Information systems → Data analytics; Data streams; • Theory of computation → Semantics and reasoning;

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Stream Reasoning for the Internet of Things

Cited by 15 publications

References 32 publications

Big data stream analysis: a systematic literature review

Big data stream analysis: a systematic literature review

Semantics in the Edge: Sensors and actuators in the Web of Linked Data and Things

Personalized Real-Time Monitoring of Amateur Cyclists on Low-End Devices

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