Information aggregation in intelligent systems: An application oriented approach

Rudas, Imre J.; Pap, Endre; Fodor, János

doi:10.1016/j.knosys.2012.07.025

Cited by 53 publications

(28 citation statements)

References 44 publications

(59 reference statements)

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“…Among these works, considerable efforts have been made on the study of aggregate functions. Mesiar et al [13], Marichal [14], and Rudas et al [1] have studied the mathematical properties of aggregate functions, such as continuity and stability, and discussed these properties of common aggregate functions in detail. A procedure for the construction of an appropriate aggregate function is also proposed by Rudas et al [1].…”

Section: Related Workmentioning

confidence: 99%

“…I N modern information systems, data aggregation, defined as the process of producing a synthesized form from multiple data items [1], is commonly applied for data processing and management. For example, in order to discover unusual patterns and infer information, a data analysis application often computes a synthesized value from a subset of the database for statistical analysis [2]; in systems dealing with large amounts of data with limited storage, the data are often aggregated to save space [3]; in a sensor network, sensor data are aggregated, and only the aggregated data are transmitted so as to save bandwidth and energy [4].…”

Section: Introductionmentioning

confidence: 99%

“…Since data aggregation plays a key role in many applications, considerable research efforts have been dedicated to this topic. A number of taxonomies have been proposed to provide a comprehensive understanding on various aspects of data aggregation, such as aggregate functions ( [1], [2], [5]), aggregation protocols ( [4], [6], [7]) and security models ( [8]). …”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

DAGGTAX: A Taxonomy of Data Aggregation Processes

Cai

Gallina

Nyström

et al. 2017

Model and Data Engineering

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Abstract-Data aggregation processes are essential constituents for data management in modern computer systems, such as decision support systems and Internet of Things (IoT) systems. Due to the heterogeneity and real-time constraints in such systems, designing appropriate data aggregation processes often demands considerable efforts. A study on the characteristics of data aggregation processes will provide a comprehensive view for the designers, and facilitate potential tool support to ease the design process. In this paper, we propose a taxonomy called DAGGTAX, which is a feature diagram that models the common and variable characteristics of data aggregation processes, especially focusing on the real-time aspect. The taxonomy can serve as the foundation of a design tool that enables designers to build an aggregation process by selecting and composing desired features, and to reason about the feasibility of the design. We also provide a set of design heuristics that could help designers to decide the appropriate mechanisms for achieving the selected features. Our industrial case study demonstrates that DAGGTAX not only strengthens the understanding, but also facilitates the model-driven design of data aggregation processes.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DAGGTAX: A Taxonomy of Data Aggregation Processes

Cai

Gallina

Nyström

et al. 2017

Model and Data Engineering

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“…Albusac et al in [1] analyzed different aggregation operators and proposed a new aggregation method based on the Sugeno integral for multiple criteria in the domain of intelligent surveillance. Also, Rudas et al in [9] offered a comprehensive study of information aggregation in intelligence systems from different application fields such as robotics, vision, knowledge based systems and data mining, etc. However, to the best of our knowledge, there is no research suggesting a set of basic principles to define an adequate operator for the aggregation process or considering the temporal influence in surveillance systems.…”

Section: Related Work and Summarymentioning

confidence: 99%

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

Liu

Hong

2015

Knowledge Science, Engineering and Management

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Abstract. In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

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“…Albusac et al in [2] analyzed different aggregation operators and proposed a new aggregation method based on the Sugeno integral for multiple criteria in the domain of intelligent surveillance. Also, Rudas et al in [13] offered a comprehensive study of information aggregation in intelligence systems from different application fields such as robotics, vision, knowledge based systems and data mining, etc. However, to the best of our knowledge, there is no research considering the decision making problem under uncertainty in surveillance systems.…”

Section: Related Work and Summarymentioning

confidence: 99%

An Extended Event Reasoning Framework for Decision Support under Uncertainty

Liu

et al. 2014

Information Processing and Management of Uncertainty in Knowledge-Based Systems

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Abstract. To provide in-time reactions to a large volume of surveillance data, uncertainty-enabled event reasoning frameworks for CCTV and sensor based intelligent surveillance system have been integrated to model and infer events of interest. However, most of the existing works do not consider decision making under uncertainty which is important for surveillance operators. In this paper, we extend an event reasoning framework for decision support, which enables our framework to predict, rank and alarm threats from multiple heterogeneous sources.

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Information aggregation in intelligent systems: An application oriented approach

Cited by 53 publications

References 44 publications

DAGGTAX: A Taxonomy of Data Aggregation Processes

DAGGTAX: A Taxonomy of Data Aggregation Processes

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

An Extended Event Reasoning Framework for Decision Support under Uncertainty

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