Trust Assessment on Streaming Data: A Real Time Predictive Approach

Peng, Tao; Sellami, Sana; Boucelma, Omar

doi:10.1007/978-3-030-65742-0_14

Cited by 3 publications

(2 citation statements)

References 30 publications

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“…However, in the real scenarios, data is unavailable. Here, we propose a method called CSIV, standing for Confidence Score for Imputed Values, to assess trust by assigning a confidence score to imputed data, which extends our previous work on data trust assessment [12]. We adopt the same definition provided in [1] stating the "Data Trustworthiness in IoT Networks is the subjective probability that data observed by a user is consistent with the data at the source", and consequently define imputed data trustworthiness as the subjective probability that imputed data is close to the expected value.…”

Section: Introductionmentioning

confidence: 89%

Trust Assessment on Data Stream Imputation in IoT Environments

Peng,

Sellami,

Boucelma

et al. 2023

Lecture Notes in Computer Science

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In the era of internet of Things, stream data emitted by sensors may rise quality issues such as incompletenes caused mainly by sensors failure or transmission problems. It is therefore necessary to recover missing data because missing values can impact decision making. Within this landscape, trust on data imputation is a key issue for helping stakeholders involved in such process. In this paper, we address the problem related to the trustworthiness on imputed data streams in IoT environments. We propose here a method called CSIV (Confidence Score for Imputed Values) to assess trust by assigning a confidence score to imputed data. CSIV considers both trust score of non-missing values and neighboring sensors. We have evaluated CSIV on real datasets using accuracy and trustworthiness as evaluation metrics. Experiments show that CSIV is able to assign correctly a trust score to the imputed values.

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Section: Introductionmentioning

confidence: 89%

Trust Assessment on Data Stream Imputation in IoT Environments

Peng,

Sellami,

Boucelma

et al. 2023

Lecture Notes in Computer Science

Self Cite

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“…They use a graph neural network to predict the traffic at intersections, where the flow from one intersection to another is represented by a graph. As mentioned in Section 1, these algorithms are based on data mining, machine learning, and time-series analysis techniques, such as in the work of Peng et al [31]. The same ideas can be extended to other kinds of transportation networks, like energy grids, where data science methods are being massively applied.…”

Section: Related Workmentioning

confidence: 99%

Time-Series-Based Queries on Stable Transportation Networks Equipped with Sensors

Bollen

Hendrix

Kuijpers

et al. 2021

IJGI

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In this paper, we propose a formalism to query transportation networks that are equipped with sensors that produce time-series data. The core of the proposed query mechanism is a logic-based language that is capable to return time, value, and time-series outputs, as well as Boolean queries. We can also use the language for node selection and path selection. Furthermore, we propose an implementation of this language in a graph database system and evaluate its working on a fragment of the Flemish river system that is equipped with sensors that measure the water height at regular moments in time.

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Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture

Kvet

Kršák

2021

Applied Sciences

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Current intelligent information systems require complex database approaches managing and monitoring data in a spatio-temporal manner. Many times, the core of the temporal system element is created on the relational platform. In this paper, a summary of the temporal architectures with regards to the granularity level is proposed. Object, attribute, and synchronization group perspectives are discussed. An extension of the group temporal architecture shifting the processing in the spatio-temporal level synchronization is proposed. A data reflection model is proposed to cover the transaction integrity with reflection to the data model evolving over time. It is supervised by our own Extended Temporal Log Ahead Rule, evaluating not only collisions themselves, but the data model is reflected, as well. The main emphasis is on the data retrieval process and indexing with regards to the non-reliable data. Undefined value categorization supervised by the NULL_representation data dictionary object and memory pointer layer is provided. Therefore, undefined (NULL) values can be part of the index structure. The definition and selection of the technology of the master index is proposed and discussed. It allows the index to be used as a way to identify blocks with relevant data, which is of practical importance in temporal systems where data fragmentation often occurs. The last part deals with the syntax of the Select statement extension covering temporal environment with regards on the conventional syntax reflection. Event_definition, spatial_positions, model_reflection, consistency_model, epsilon_definition, monitored_data_set, type_of_granularity, and NULL_category clauses are introduced. Impact on the performance of the data manipulation operations is evaluated in the performance section highlighting temporal architectures, Insert, Update and Select statements forming core performance characteristics.

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Trust Assessment on Streaming Data: A Real Time Predictive Approach

Cited by 3 publications

References 30 publications

Trust Assessment on Data Stream Imputation in IoT Environments

Trust Assessment on Data Stream Imputation in IoT Environments

Time-Series-Based Queries on Stable Transportation Networks Equipped with Sensors

Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture

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