Real-Time Data Analytics and Event Detection for IoT-Enabled Communication Systems

Ali, Mohammad; Ono, Naomi; Kaysar, Mahedi; Shamszaman, Zia Ush; Pham, Thu-Le; Gao, Feng; Griffin, Keith; Mileo, Alessandra

doi:10.2139/ssrn.3199128

Cited by 5 publications

(11 citation statements)

References 36 publications

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“…Their stream reasoning component is tested only with small amounts of input data. Our initial experimental results on StreamJess show better performance than the OClingo component implemented in StreamRule and (Ali et al, 2016) for small inputs, while system's performance evaluation for larger inputs is part of our future works. Jess is memory-intensive application, but recent Java Virtual Machines include flexible and configurable garbage collection subsystem which is responsible for finding and deleting unused objects (Hill, 2003).…”

Section: Hybrid Approachesmentioning

confidence: 90%

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StreamJess: a stream reasoning framework for water quality monitoring

Jajaga

Ahmedi

2016

IJMSO

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Stream data knowledge bases modeled with OWL are a proved natural approach. But, querying and reasoning over these knowledge bases is not supported with standard Semantic Web technologies like SPARQL and SWRL. Query processing systems enable querying, but to the best of our knowledge, Semantic Web rules are still unable to handle the required reasoning features for effective inference over stream data i.e. non-monotonic, closed-world and time-aware reasoning. In absence of such system, we showed in our previous work how Jess can be used for monitoring water quality, but by bringing input data manually. In this paper, we enable stream data support and thus a timely detection of faulty water quality statuses. The system also identifies the potential sources of pollution by also extending our ontology with the pollutants module. The solution utilizes C-SPARQL abilities to filter and aggregate RDF streams on windows to enable closed-world and time-aware reasoning with Jess rules. Moreover, Jess Tab functions are used to enable non-monotonic behavior.

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Section: Hybrid Approachesmentioning

confidence: 90%

“…Recently, Ali et al (2016) describe the descendant of StreamRule, which support C-SPARQL aside of CQELS. The system supports reasoning even in incomplete information cases through NAF, but like StreamRule it does not support historical data management.…”

Section: Hybrid Approachesmentioning

confidence: 99%

StreamJess: a stream reasoning framework for water quality monitoring

Jajaga

Ahmedi

2016

IJMSO

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“…To represent and store rules in each node's KB, SHACL formalism is used. 7 http://onem2m.org/ Let us consider a home automation example application needing to be informed about the level of comfort of each room. The high level information useful for this application can be expressed in a rule, made simple for the sake of clarity, such as "If in a room the observed luminosity is above 400Lx and the observed temperature is between 20 and 25 degrees, then this room is comfortable".…”

Section: B Knowledge Considered By Nodes Applying Edrmentioning

confidence: 99%

“…IoT applications, e.g an automatic light manager or a smart city traffic monitor, exploit data collected by device networks for ad-hoc purposes. These applications usually consume IoT data from Cloud nodes, processed in order to provide highlevel information relevant to the application end user [7]. Rules can be used to capture the specific needs of applications by representing and sharing dedicated deduction intent [8].…”

Section: Introductionmentioning

confidence: 99%

A Distributed Scalable Approach for Rule Processing: Computing in the Fog for the SWoT

Seydoux

Drira

Hernandez

et al. 2018

2018 IEEE/WIC/ACM International Conference on Web Intelligence (WI)

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The development of the Semantic Web of Things (SWoT) is challenged by the nature of IoT deployment architectures, where constrained devices collect data processed remotely by powerful Cloud servers. Such a deployment pattern introduces bottlenecks constituting a hurdle for scalability, and increases response time. This hinders the development of a number of critical and time-sensitive applications. Enabling the deployment of the Semantic Web stack closer to the constrained devices of the IoT may foster the development of time-sensitive interoperable applications, while reducing forwarding the user data to remote third party Cloud servers. The approach we develop in this paper is a contribution towards this direction, and aims to enable rulebased reasoning closer to sensors producing IoT data. For this purpose, we define a distributed scalable semantic processing algorithm by dynamically propagating deduction rules on Fog nodes. Our goal is to shorten the time needed to deliver high level information deduced from the collected data. This approach is evaluated on a smart building use case where both distribution and scalability have been considered.

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“…These time-critical systems need to react as quick as possible to newly generated event data. However, many of these systems need to integrate background knowledge with the event data on the fly, to enable real-time interpretation of these events and execute advanced logics to make correct decisions [2], [3]. For instance, in an IoT nurse call system, expressive reasoning is required to capture the capabilities of the nurse, the pathologies of the patients, the relation between the patients and the staff, etc [4].…”

Section: Introduction a Problem Descriptionmentioning

confidence: 99%

Subset Reasoning for Event-Based Systems

2019

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In highly dynamic domains such as the Internet of Things (IoT), smart industries, smart manufacturing, pervasive health or social media, data is being continuously generated. By combining this generated data with background knowledge and performing expressive reasoning upon this combination, meaningful decisions can be made. Furthermore, this continuously generated data typically originates from multiple heterogeneous sources. Ontologies are ideal for modeling the domain and facilitates the integration of heterogeneous produced data with background knowledge. Furthermore, expressive ontology reasoning allows to infer implicit facts and enables intelligent decision making. The data produced in these domains is often volatile. Time-critical systems, such as IoT Nurse Call systems, require timely processing of the produced IoT data. However, there is still a mismatch between volatile data and expressive ontology reasoning, since the incoming data frequency is often higher than the reasoning time. For this reason, we present an approximation technique that allows to extract a subset of data to speed-up the reasoning process. We demonstrate this technique in a Nurse Call proof of concept where the locations of the nurses are tracked and the most suited nurse is selected when the patient launches a call and in an extension of an existing benchmark. We managed to speed up the reasoning process up to 10 times for small datasets and up to more than 1000 times for large datasets. INDEX TERMS Reasoning, streams, event-based, ontology, nurse call.

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Real-Time Data Analytics and Event Detection for IoT-Enabled Communication Systems

Cited by 5 publications

References 36 publications

StreamJess: a stream reasoning framework for water quality monitoring

StreamJess: a stream reasoning framework for water quality monitoring

A Distributed Scalable Approach for Rule Processing: Computing in the Fog for the SWoT

Subset Reasoning for Event-Based Systems

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