Aggregation of imprecise and uncertain information in databases

Radio Frequency Identification (RFID) is widely used to track and trace objects in supply chain management. However, massive uncertain data produced by RFID readers are not suitable for directly use in RFID applications. Following our thorough analysis of key features of RFID objects, this paper proposes a new framework for effectively and efficiently processing uncertain RFID data, and supporting a variety of queries for tracking and tracing RFID objects. In particular, we propose an adaptive cleaning method by adjusting size of smoothing window according to various rates of uncertain data, employing different strategies to process uncertain readings, and distinguishing different types of uncertain data according to their appearing positions. We propose a comprehensive data model, which is suitable for a wide range of application scenarios. In addition, a path coding scheme is proposed to significantly compress massive data by aggregating the path sequences, the positions and the time intervals. Experimental evaluations show that our approach is effective and efficient in terms of the compression and traceability queries.KeywordsSupply Chain ManagementUncertain DataElectronic Product CodeSmoothing WindowPath SequenceThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Most geostatistical estimation and simulation methodologies assume the experimental data as hard measurements, meaning that the measures of a given property of interest are not associated with uncertainty. The challenge of integrating uncertain experimental data at the geostatistical estimation or simulation models is not new. Several attempts have been made, either considering the uncertain data as soft data or interpreting it as inequality constraints, based on the indicator formalism or decreasing the weight of soft data in kriging procedures. This paper presents a stochastic simulation methodology where the uncertain experimental data are modelled by a probability distribution at each sample location. Data values are firstly drawn, by stochastic simulation, at these locations prior to the simulation of the rest of the grid nodes. This method is also extended to the simulation of categorical uncertain data, as well as to the simulation with uncertain block support data. To illustrate the proposed methodology, an application to a real case study of pore pressure prediction of oil reservoirs is presented, as well as an upscaling problem.

Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. It is important and significant to review the contributions of fuzzy deep learning for uncertain medical data, because fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the presence of noise, artifact or high dimensional unstructured information in uncertain medical data. Therefore, taking focus on the intersection of both different fuzzy deep learning models and several types of uncertain medical data, the paper first constructs four types of frameworks of fuzzy deep learning models used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning models, uncertain medical data and application scenarios. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. This work has some original points: (1) four types of frameworks of applying fuzzy deep learning models for uncertain medical data are first proposed. (2) Seven fuzzy deep learning models, five types of uncertain medical data, and five application scenarios are reviewed in details, respectively. (3) The advantages, challenges, and future research directions of fuzzy deep learning for uncertain medical data are critically analyzed, providing valuable suggestions for further deep research.

A large number of intelligent network control protocols are used in the field of the internet of things. But a variety of network protocols have different data formats, inconsistent transmission mode and various solutions, etc. Especially the processing of uncertain data. To integrate IP networks, ZigBee wireless sensor network and the GPRS mobile phone network data, a solution is proposed to deal with the fusion of a variety of heterogeneous network protocol data in the paper, which achieves seamless switching and forwarding data between heterogeneous networks under multi-protocol. For the uncertain data obtained by the sensors, an algorithm is also proposed, the correlation calculation and determination algorithm of uncertain data streams which determines the credibility of these data. The final decision-making conclusions will be done based on judgment decisions by solution rules. Based on the two above methods, the smart home control system is designd and realized in this study. The results show that system and algorithms are feasible and validate, it achieves seamless switching and forwarding of data between heterogeneous networks and effectively identifies the uncertain data.

Nowadays, due to the growth of technology, there is a mass production of data (of large volume), available in a digital form. These currently available data are not unified but appear in different formats and types. The diversity of the data is based on the type of information, they contain, such as text, image, video and audio documents and also on their sources, such as data from sensors (high variety). In addition, with the expansion of the Internet and the World Wide Web, the majority of these data become the publicity available for a wide range of users (at high speed). The main objective of this work is to propose an efficient Big Uncertainty Web Data Services Indexing Model able to reasoning in uncertain data environment. More concretely, the proposed approach is based on two main phases: the first one consists on processing uncertain data in the syntactic indexing phase and the second one consists on the semantic indexing phase. These two phases are presented as two algorithms syntactic and semantic.

Data uncertainty is widespread in a variety of applications. This paper proposes a new Bayesian classification algorithm for classifying uncertain data. In the paper, we apply probability and statistics theory on uncertain data model, and provide solutions for model parameter estimation for both uncertain numerical data and uncertain categorical data. We also prove the correctness of the solutions. The experimental results demonstrate the proposed uncertain Bayesian classifier can be efficiently constructed, and it significantly outperforms the traditional Bayesian classifier in prediction accuracy when data is highly uncertain.

It is generally accepted that data production has experienced spectacular growth for several years due to the proliferation of new technologies such as new mobile devices, smart meters, social networks, cloud computing and sensors. In fact, this data explosion should continue and even accelerate. To find all of the documents responding to a request, any information search system develops a methodology to confirm whether or not the terms of each document correspond to those of the user's request. Most systems are based on the assumption that the terms extracted from the documents have been certain and precise. However, there are data in which this assumption is difficult to apply. The main objective of the work carried out within the framework of this article is to propose a new model of data service indexing in an uncertain environment, meaning that the data they contain can be untrustworthy, or they can be contradictory to another data source, due to failure in collection or integration mechanisms. The solution we have proposed is characterized by its Intelligent side ensured by an efficient fuzzy module capable of reasoning in an environment of uncertain and imprecise data. Concretely, our proposed approach is articulated around two main phases: (i) a first phase ensures the processing of uncertain data in a textual document and, (ii) the second phase makes it possible to determine a new method of uncertain syntactic indexing. We carried out a series of experiments, on different bases of standard tests, in order to evaluate our solution while comparing it to the approaches studied in the literature. We used different standard performance measures, namely precision, recall and . The results found showed that our solution is more efficient and more efficient than the main approaches proposed in the literature. The results show that the proposed approach realizes an efficient Big Data indexing solution in an Uncertain Environment that increases the Precision, the Recall and the measurements. Experimental results present that the proposed uncertain model obtained the best precision accuracy 0.395 with KDD database and the best recall accuracy 0.254 with the same database.

The communication among heterogeneous embedded devices could lead to correctness problems in M2M environment. Sometimes, it is not easy to classify the data because they may provide wrong or uncertain information. The data from these devices should be gathered in a safe, efficient, and right manner without the help of server or human intervention; even the low-level information from each device causes interoperability problems. This data gathering or data fusion process is very important because the data mapping result could be understood as totally different situation and hence cause different reaction, feedback, and controls. In this paper, we propose a hierarchical aggregation for uncertain sensor data using reinforcement learning to get correct and efficient data gathering result for reliable wireless sensor network. In our proposal, we add a new category for uncertain data and classify them through reinforcement learning using hierarchical subcategories. By adopting our proposed aggregation, false classification caused by uncertain data can be decreased and the correctness of data gathering can be enhanced.

The potential applications and challenges of uncertain data mining have recently attracted interests from researchers. Most uncertain data mining algorithms consider aleatory (random) uncertainty of data, i.e. these algorithms require that exact probability distributions or confidence values are attached to uncertain data. However, knowledge about uncertainty may be incomplete in the case of epistemic (incomplete) uncertainty of data, i.e. probabilities of uncertain data may be imprecise, coarse, or missing in some applications. The paper focuses on uncertain data which miss probabilities, specially, value-uncertain discrete objects which miss probabilities (for short uncertain objects). On the other hand, classification is one of the most important tasks in data mining. But, to the best of our knowledge, there is no method to learn Naïve Bayesian classifier from uncertain objects. So the paper studies Naïve Bayesian classification of uncertain objects. Firstly, the paper defines interval probabilities of uncertain objects from probabilistic cardinality point of view, and bridges the gap between uncertain objects and the theory of interval probability by proving that interval probabilities are F-probabilities. Secondly, based on the theory of interval probability, the paper defines conditional interval probabilities including the intuitive concept and the canonical concept, and the conditional independence of the intuitive concept. Further, the paper gives a formula to effectively compute the intuitive concept. Thirdly, the paper presents a Naïve Bayesian classifier with interval probability parameters which can handle both uncertain objects and certain objects. Finally, experiments with uncertain objects based on UCI data show satisfactory performances.

Managing sustainability in modern supply chains requires decision-making tools that can accommodate conflicting criteria, uncertain data, and evaluations that involve both positive and negative impacts. To address these challenges, this study develops a new decision-making framework based on bipolar complex Pythagorean fuzzy sets (BCPFSs). The model integrates bipolarity, complex-valued membership degrees, and Pythagorean structures to capture the nuanced interplay of economic, environmental, and social considerations. On this foundation, two aggregation operators—the bipolar complex Pythagorean fuzzy weighted averaging (BCPFWA) and weighted geometric (BCPFWG) operators—are introduced to synthesize multidimensional information while preserving uncertainty and dual evaluations. The applicability of the framework is demonstrated through a case study on green supply chain management (GSCM). Six supplier strategies, ranging from cost-oriented to fully balanced sustainability-focused approaches, are assessed against eight attributes including cost efficiency, product quality, carbon emissions, waste management, technological integration, and social responsibility. The analysis reveals that the balanced sustainability supplier emerges as the most effective choice, consistently ranked highest by both operators. Comparative results with conventional fuzzy aggregation approaches show that the proposed operators provide richer, more stable, and more interpretable rankings, especially when trade-offs between cost and sustainability are present. This research contributes to both theory and practice: it extends the scope of fuzzy decision-making by unifying multiple existing models as special cases, and it offers a practical toolset for organizations seeking resilient and environmentally responsible supply chain solutions. The findings demonstrate that BCPF-based aggregation can enhance strategic decision-making in contexts where sustainability and uncertainty are inseparably linked.

Uncertain data streams can have tuples with both value and existential uncertainty. A tuple has value uncertainty when it can assume multiple possible values. A tuple is existentially uncertain when the sum of the probabilities of its possible values is $$<$$ 1. A situation where existential uncertainty can arise is when applying relational operators to streams with value uncertainty. Several prior works have focused on querying and mining data streams with both value and existential uncertainty. However, none of them have studied, in depth, the implications of existential uncertainty on sliding window processing, even though it naturally arises when processing uncertain data. In this work, we study the challenges arising from existential uncertainty, more specifically the management of count-based sliding windows, which are a basic building block of stream processing applications. We extend the semantics of sliding window to define the novel concept of uncertain sliding windows and provide both exact and approximate algorithms for managing windows under existential uncertainty. We also show how current state-of-the-art techniques for answering similarity join queries can be easily adapted to be used with uncertain sliding windows. We evaluate our proposed techniques under a variety of configurations using real data. The results show that the algorithms used to maintain uncertain sliding windows can efficiently operate while providing a high-quality approximation in query answering. In addition, we show that sort-based similarity join algorithms can perform better than index-based techniques (on 17 real datasets) when the number of possible values per tuple is low, as in many real-world applications.

Risk assessment of offshore gas pipelines is a challenging task due to uncertain and vague data on failure mechanisms and the effects of events. The goal of this paper is to develop a systematic approach to overcome this uncertainty. The likelihood of a failure event is obtained via fault tree analysis, and the associated failure consequences are obtained via event tree analysis. To cope with uncertainties, the fuzzy set theory has been combined with the fault tree analysis and event tree analysis. Financial consequences of offshore pipeline failure are estimated and financial consequences are divided into economic losses, environmental damage, and human health issue. To assess the feasibility of this method, an example of its application in an offshore pipeline containing sour gas is presented. Flash fire, pool fire, and environmental damage are the most known consequences associated with offshore pipeline failure. Therefore, application of fuzzy set theory in the likelihood assessment of pipeline failure and severity of the resulting consequence can be used to incorporate uncertain and imprecise data in this field.

Reporting l most influential objects in uncertain databases based on probabilistic reverse top-k queries

This paper investigates the detailed analysis of linear diophantine fuzzy Aczel-Alsina aggregation operators, enhancing their efficacy and computational efficiency while aggregating fuzzy data by using the fuzzy C-means (FCM) method. The primary goal is to look at the practical uses and theoretical foundations of these operators in the context of fuzzy systems. The aggregation process is optimised using the FCM algorithm, which divides data into clusters iteratively. This reduces computer complexity and enables more dependable aggregation. The mathematical underpinnings of Linear Diophantine Fuzzy Aczel-Alsina aggregation operators are thoroughly examined in this study, along with an explanation of their purpose in handling imprecise and uncertain data. It also investigates the integration of the FCM method, assessing its impact on simplifying the aggregation procedure, reducing algorithmic complexity, and improving the accuracy of aggregating fuzzy data sets. This work illuminates these operators performance and future directions through extensive computational experiments and empirical analysis. It provides an extensive framework that shows the recommended strategy’s effectiveness and use in a variety of real-world scenarios. We obtain our ultimate outcomes through experimental investigation, which we use to inform future work and research. The purpose of the study is to offer academics and practitioners insights on how to improve information fusion techniques and decision-making processes.

The classifications of uncertain data turned into one of the dreary procedures in the data mining domain. The uncertain data have tuples with distinctive probability distribution, which helps to find similar class of tuples. When we consider an uncertain data, the feature vector will not be a single valued but a function. In this paper, we proposed fuzzy entropy and similarity measure to characterize the uncertain data through binary decision tree algorithm. Fuzzy entropy is used to find the best split point for the decision tree to handle the uncertain data. Similarity measure is used to make the better decision for the uncertain data with high accuracy. Initially, fuzzy entropy for each feature vector is calculated to select the best feature vector. Then, best split is selected from the selected feature vector. With the help of trained uncertain data, the binary tree starts to grow. Once the split point is selected, then the constructed decision tree is evaluated by the testing phase of uncertain data. The testing data are subjected to the trained decision tree to obtain the classified data. The experimental analyses are made to evaluate the performance of the proposed FUDT approach. Proposed FUDT algorithm is compared with the existing classification algorithm UDT in terms of accuracy and running time. The experimental analysis finalizes that our FUDT algorithm outperforms the existing UDT algorithm.