Search

More and more IoT data is being traded online in cloud-based data marketplaces due to the fast-growing market demand. Within the current data selling mechanisms, data consumers have difficulties in making purchasing decisions due to uncertain data quality and inflexible pricing interface. To resolve them, potential solutions could be to launch data demonstrations and release free sampling data to reduce the uncertainty about data quality, and to charge based on the volume of data for enabling flexible pricing. However, their economic benefits are still not clear. In this paper, we design the optimal data selling mechanisms for IoT data exchange, and derive the following results. First, whether to deploy data demonstrations and how much free sampling data to release depend on the extent of data consumers inaccuracy perceptions for data quality. We found that data vendors have no incentive to conduct these strategies if data consumers extremely overestimate data quality. Second, although flexible data pricing mechanisms provide convenience for real-time and streaming IoT data exchange, it brings less economic benefits to data vendors compared with fixed pricing schemes. We evaluate the optimal selling mechanisms on a real-world Taxi GPS dataset. Evaluation results verify the insights derived from our theoretical analysis.

Feature selection has been studied by many researchers using information theory to select the most informative features. Up to now, however, little attention has been paid to the interactivity and complementarity between features and their relationships. In addition, most of the approaches do not cope well with fuzzy and uncertain data and are not adaptable to the distribution characteristics of data. Therefore, to make up for these two deficiencies, a novel interactive and complementary feature selection approach based on fuzzy multineighborhood rough set model (ICFS_FmNRS) is proposed. First, fuzzy multineighborhood granules are constructed to better adapt to the data distribution. Second, feature multicorrelations (i.e., relevancy, redundancy, interactivity, and complementarity) are considered and defined comprehensively using fuzzy multigranularity uncertainty measures. Next, the features with interactivity and complementarity are mined by the forward iterative selection strategy. Finally, compared with the benchmark approaches on several datasets, the experimental results show that ICFS_FmNRS effectively improves the classification performance of feature subsets while reducing the dimension of feature space.

Visually identifying effective bio-markers from human brain networks poses non-trivial challenges to the field of data visualization and analysis. Existing methods in the literature and neuroscience practice are generally limited to the study of individual connectivity features in the brain (e.g., the strength of neural connection among brain regions). Pairwise comparisons between contrasting subject groups (e.g., the diseased and the healthy controls) are normally performed. The underlying neuroimaging and brain network construction process is assumed to have 100% fidelity. Yet, real-world user requirements on brain network visual comparison lean against these assumptions. In this work, we present MV^2Net, a visual analytics system that tightly integrates multi-variate multi-view visualization for brain network comparison with an interactive wrangling mechanism to deal with data uncertainty. On the analysis side, the system integrates multiple extraction methods on diffusion and geometric connectivity features of brain networks, an anomaly detection algorithm for data quality assessment, single- and multi-connection feature selection methods for bio-marker detection. On the visualization side, novel designs are introduced which optimize network comparisons among contrasting subject groups and related connectivity features. Our design provides level-of-detail comparisons, from juxtaposed and explicit-coding views for subject group comparisons, to high-order composite view for correlation of network comparisons, and to fiber tract detail view for voxel-level comparisons. The proposed techniques are inspired and evaluated in expert studies, as well as through case analyses on diffusion and geometric bio-markers of certain neurology diseases. Results in these experiments demonstrate the effectiveness and superiority of MV^2Net over state-of-the-art approaches.

Uncertain data has been emerged as an important problem in database systems due to the imprecise nature of many applications. To handle the uncertainty, probabilistic databases can be used to store uncertain data, and querying facilities are provided to yield answers with confidence. However, the uncertainty may propagate, hence the results from a query or mining process may not be useful. In this paper, we leverage the power of crowdsourcing by designing a set of Human Intelligence Tasks (HITs) to ask a crowd with diverse accuracy rates, to improve the quality of uncertain data. Each HIT is associated with a cost, thus, we need to design solutions to maximize the data quality with minimal number of HITs. There are two obstacles for this non-trivial optimization, which lead to very high computational cost for selecting the optimal set of HITs. First, members of a crowd may return incorrect answers with different probabilities. Second, the HITs decomposed from uncertain data are often correlated. We have addressed these challenges in this paper by designing an effective approximation algorithm and an efficient heuristic solution, even under diverse individual accuracy rates of the crowdsourcing workers.

Lately, variational autoencoder (VAE) has arisen as one of the most prevalent methods for unsupervised learning of intricate distributions. Despite being successful in deep feature extraction and uncertain data modelling, it still suffers from the instability and reconstruction error due to random sampling in the latent subspace representation of original input space. In this paper, to deal with those limitations, constrained VAE (CVAE) is proposed by utilizing input sample information. Enthused by parallel interaction mechanism between the ventral and dorsal stream of human brain in object recognition, parallel interaction spatial-temporal CVAE (PIST-CVAE) is proposed to extract spatial and temporal features from input samples. Lower dimensional nonlinear features extracted from PIST-CVAE are used to build the soft sensor. The effectiveness of CVAE and PIST-CVAE is demonstrated in an industrial case study, a polyester polymerization process.

Pattern mining is a fundamental technique of data mining to discover interesting correlations in the data set. There are several variations of pattern mining, such as frequent itemset mining, sequence mining, and high utility itemset mining. High utility itemset mining is an emerging data science task, aims to extract knowledge based on a domain objective. The utility of a pattern shows its effectiveness or benefit that can be calculated based on user priority and domain-specific understanding. The sequential pattern mining (SPM) issue is much examined and expanded in various directions. Sequential pattern mining enumerates sequential patterns in a sequence data collection. Researchers have paid more attention in recent years to frequent pattern mining over uncertain transaction dataset. In recent years, mining itemsets in big data have received extensive attention based on the Apache Hadoop and Spark framework. This paper seeks to give a broad overview of the distinct approaches to pattern mining in the Big Data domain. Initially, we investigate the problem involved with pattern mining approaches and associated techniques such as Apache Hadoop, Apache Spark, parallel and distributed processing. Then we examine major developments in parallel, distributed, and scalable pattern mining, analyze them in the big data perspective and identify difficulties in designing the algorithms. In particular, we study four varieties of itemsets mining, i.e., parallel frequent itemsets mining, high utility itemset mining, sequential patterns mining and frequent itemset mining in uncertain data. This paper concludes with a discussion of open issues and opportunity. It also provides direction for further enhancement of existing approaches.

The outsourcing of data is becoming increasingly commonplace as data is constantly been synchronized between user systems (e.g., personal computers and sensor devices) and cloud computing servers. However, for the consideration of data privacy, it is necessary to encrypt sensitive data prior to outsourcing. Limitations such as measurement, network delays, and data obfuscation may, however, result in uncertain data. Compared with searching over encrypted certain data, processing queries for encrypted uncertain data is more challenging. In this paper, we propose a secure and efficient range query scheme over outsourced encrypted uncertain data, for example from Internet of Things (IoT) systems. Specifically, we use pivot mapping to map data to a low-dimensional space to facilitate calculation and processing while preserving some of the original relevance among the data. Additionally, we encode data and then map codes into multiple Bloom filters which are organized by a binary tree-based index. Our scheme achieves data privacy, hides the relevance among data, and also supports efficient queries. We analyze the security and evaluate the performance of our approach using experiments on Microsoft Azure. The analysis and experimental results evidence that our proposed approach is secure and efficient.

Given that the three-way decision (3WD) theory provides a scientific solution and reasonable semantic interpretation for solving multi-criteria decision-making (MCDM) problems, this paper presents a new 3WD model and applies it to realistic MCDM problems. Since the fuzzy neighborhood operator is effective in handling uncertain data, we first define a pair of probabilistic approximation operators via the constructed α -fuzzy neigh borhood class. Then, a 3WD model is explored in decision information systems to address the related issues in the proposed fuzzy probabilistic rough set model. In light of the above works, we establish a novel approach to classify and rank applicants for enterprise talent recruitment problems. Instead of the rule of tie-breaking, a way is given to separate applicants into three parts by combining decision attributes , and the feasibility of the approach is confirmed as well. Finally, by using a data set in the UCI database, the results of comparative and experimental analyses demonstrate that the constructed MCDM approach owns better performances in terms of effectiveness and stability.

Uncertainty evaluation is a core technique when deep neural networks (DNNs) are used in real-world problems. In practical applications, we often encounter unexpected samples that have not seen in the training process. Not only achieving the high-prediction accuracy but also detecting uncertain data is significant for safety-critical systems. In statistics and machine learning, Bayesian inference has been exploited for uncertainty evaluation. The Bayesian neural networks (BNNs) have recently attracted considerable attention in this context, as the DNN trained using dropout is interpreted as a Bayesian method. Based on this interpretation, several methods to calculate the Bayes predictive distribution for DNNs have been developed. Though the Monte-Carlo method called MC dropout is a popular method for uncertainty evaluation, it requires a number of repeated feed-forward calculations of DNNs with randomly sampled weight parameters. To overcome the computational issue, we propose a sampling-free method to evaluate uncertainty. Our method converts a neural network trained using dropout to the corresponding Bayesian neural network with variance propagation. Our method is available not only to feed-forward NNs but also to recurrent NNs such as LSTM. We report the computational efficiency and statistical reliability of our method in numerical experiments of language modeling using RNNs, and the out-of-distribution detection with DNNs.

Cloud Manufacturing is customer-oriented manufacturing under uncertain environments, and it is significant to enhance customers satisfaction. However, previous studies had not paid enough attention to the nonrational customer under uncertain environments, and we design customer-oriented fuzzy scheduling based on the cloud model. First, previous studies had mainly studied deterministic scheduling, but fuzzy scheduling had not attracted enough attention. Because of the uncertain environment, we study fuzzy scheduling in Cloud Manufacturing. Second, for fuzzy scheduling, the previous uncertain forms had difficulty presenting the distributed features of collected data, we use the cloud model to perform the uncertain data. Third, previous studies proposed that satisfaction was linear with the index value, such as tardiness or earliness, but nonrational customers were not considered. We use prospect theory to calculate the satisfaction of nonrational customers. Finally, to solve the scheduling problem, we design an enhanced gray wolf optimization with a parallel searching mechanism. The advantages of our algorithm are verified by comparison with other similar algorithms.

Rapid advancements in our innovation have encouraged early analysis of diseases in the clinical area. The primary diagnostic tool for detecting cardiovascular diseases is electrocardiogram. We have proposed two classifiers for classifying six types of heartbeats. Among these six types, one is normal, and the other five depict cardiac arrhythmia beats. For tackling complex order issues, decision trees are a notable methodology in all artificial intelligence procedures. The standard decision tree algorithms are unable to process imprecise, uncertain and incomplete data. Optimizing hyperparameters helps to overcome these flaws. Hence, an optimized decision tree and adaptive boosted optimized decision tree are proposed to deal with the data with uncertain class labels and attribute values. Moreover, we consider the instance where the uncertain data are characterized and accomplished over the evidence theory. The proposed method is validated and tested on MIT-BIH arrhythmia database. Our proposed model is effective and promising and outperformed all state-of-the-art techniques with an accuracy of 98.77%.

In periodontology, full–mouth examination is the gold standard protocol for disease diagnosis. It involves an examination of six sites on every existing tooth, to measure a set of standard parameters. Thus, it is an exhaustive task that bothers both the examiner and the patient. On the other hand, as the data are numerical, measurement faults may mislead the diagnosis process. In this paper, we make use of the flexibility of fuzzy logic in handling noisy and uncertain data and propose a new classification scheme that is aimed at estimating a set of periodontal parameters based on some others. The training dataset includes full-mouth parameters of about 600 patients recorded in Shiraz Dental School. We also propose an effective use rule-weight learning method to tune the classifier. Using the proposed scheme, the following three types of estimations were carried out, accurately: estimation of some parameters for a tooth using some other parameters of itself (Type-I), estimation of a tooth’s parameters from the parameters of its adjacent teeth (Type-II), and estimation of a tooth’s parameters from the parameters of the same # teeth on opposite quadrants (Type-III). The highest values of dependency and thus the minimum estimation errors were observed for Type-I dependencies, followed by Type-III and Type-II ones, respectively. The results of ROC analysis also showed the highest AUC value (0.91) for Type-I classifiers, while Type-II and Type-III classifiers resulted in AUC values of 0.86 and 0.81. The specific findings about the existing relationships will be reported within the manuscript.

Clustering uncertain data is an essential task in data mining and machine learning. Possible world based algorithms seem promising for clustering uncertain data. However, there are two issues in existing possible world based algorithms: (1) They rely on all the possible worlds and treat them equally, but some marginal possible worlds may cause negative effects. (2) They do not well utilize the consistency among possible worlds, since they conduct clustering or construct the affinity matrix on each possible world independently. In this paper, we propose a representative possible world based consistent clustering (RPC) algorithm for uncertain data. First, by introducing representative loss and using Jensen–Shannon divergence as the distribution measure, we design a heuristic strategy for the selection of representative possible worlds, thus avoiding the negative effects caused by marginal possible worlds. Second, we integrate a consistency learning procedure into spectral clustering to deal with the representative possible worlds synergistically, thus utilizing the consistency to achieve better performance. Experimental results show that our proposed algorithm outperforms existing algorithms in effectiveness and performs competitively in efficiency.

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

Scheduling workflows in cloud service environment has attracted great enthusiasm, and various approaches have been reported up to now. However, these approaches often ignored the uncertainties in the scheduling environment, such as the uncertain task start/execution/finish time, the uncertain data transfer time among tasks, the sudden arrival of new workflows. Ignoring these uncertain factors often leads to the violation of workflow deadlines and increases service renting costs of executing workflows. This study devotes to improving the performance for cloud service platforms by minimizing uncertainty propagation in scheduling workflow applications that have both uncertain task execution time and data transfer time. To be specific, a novel scheduling architecture is designed to control the count of workflow tasks directly waiting on each service instance (e.g., virtual machine and container). Once a task is completed, its start/execution/finish time are available, which means its uncertainties disappearing, and will not affect the subsequent waiting tasks on the same service instance. Thus, controlling the count of waiting tasks on service instances can prohibit the propagation of uncertainties. Based on this architecture, we develop an unce R tainty-aware O nline S cheduling A lgorithm ( ROSA ) to schedule dynamic and multiple workflows with deadlines. The proposed ROSA skillfully integrates both the proactive and reactive strategies. During the execution of the generated baseline schedules, the reactive strategy in ROSA will be dynamically called to produce new proactive baseline schedules for dealing with uncertainties. Then, on the basis of real-world workflow traces, five groups of simulation experiments are carried out to compare ROSA with five typical algorithms. The comparison results reveal that ROSA performs better than the five compared algorithms with respect to costs (up to 56 percent), deviation (up to 70 percent), resource utilization (up to 37 percent), and fairness (up to 37 percent).

Natural or man-made disasters impose destructive effects like human injuries and urban infrastructure damages, which lead to disruptions that affect the entire distribution system. This research addresses the routing-allocation problem in the response phase of disaster management. The related literature shows that the researchers had less attention to some features like sustainability and resiliency in the mentioned problem. Hence, to cover these gaps, this study proposes a scenario-based multi-objective programming model to examine the resilient-sustainable routing-allocation problem considering the concept of fairness. The proposed model aims at minimizing total traveling time, total environmental impacts and total demand loss. The fuzzy robust stochastic optimization approach is utilized to cope with uncertain data arisen in disaster conditions. Then, due to the complexity of the research problem, a hybrid approach based on the multi-choice goal programming method and a heuristic algorithm is developed to solve the problem in a reasonable time. A case study is then selected to demonstrate the efficiency of the proposed model and the developed method. Finally, sensitivity analyses have been conducted in some parameters of the model and also the robustness of the solution has been investigated.

The farfetched certain classification of uncertain data suffers serious risks. Three-Way Decision (3WD) theory is utilized to implement uncertain data classification methods. Three-way uncertain data classification methods facilitate reducing decision risk and involving human–machine coordination through finding out uncertain cases for abstaining identification. Due to the limitation of traditional classifiers in feature learning, most existing three-way uncertain data classification methods are not good at handling the unstructural data of digital images. This shortage hinders the applications of three-way uncertain data classification in image-based decision support systems, such as the medical decision support systems based on radiographs. In this paper, we adopt deep convolutional neural networks (DCNNs) for feature learning and Dempster–Shafer (D-S) evidence theory as uncertainty measure to implement a three-way method for image classification. We utilize evidence theory to measure the uncertainty of the predictions produced by DCNNs and construct a novel evidential deep convolutional neural network (EviDCNN). Based on this, we propose a Three-Way Classification method with EviDCNN (EviDCNN-3WC). The experiments on massive medical image data sets validate that the proposed three-way classification method with EviDCNN is effective to identify uncertain images and reduce the risk in image classification. The superiorities of the proposed method facilitate its applications in image-based medical decision support systems.

Typically, a search operation on Big Data is complicated task due to different formats and different nature of the data. To simplify it the user may implement special algorithms to process different data for uncertain criteria. The “self-learning query” algorithm presented in this work allows to search in Big Data either for certain or uncertain search criteria’s with minimum attention from the data programmer. It uses accumulated search statistics as the basis to make the result set more precisely according to the search criteria, so as long the user work with the system as more precise will be the results. The algorithm presented in this work allows data scientists to search for uncertain data and potentially discover results faster by offloading the burdens of data management and provenance to the expert system.

We present an approach to discriminate SARS-CoV-2 virus types based on their RNA sequence descriptions avoiding a sequence alignment. For that purpose, sequences are preprocessed by feature extraction and the resulting feature vectors are analyzed by prototype-based classification to remain interpretable. In particular, we propose to use variants of learning vector quantization (LVQ) based on dissimilarity measures for RNA sequence data. The respective matrix LVQ provides additional knowledge about the classification decisions like discriminant feature correlations and, additionally, can be equipped with easy to realize reject options for uncertain data. Those options provide self-controlled evidence, i.e., the model refuses to make a classification decision if the model evidence for the presented data is not sufficient. This model is first trained using a GISAID dataset with given virus types detected according to the molecular differences in coronavirus populations by phylogenetic tree clustering. In a second step, we apply the trained model to another but unlabeled SARS-CoV-2 virus dataset. For these data, we can either assign a virus type to the sequences or reject atypical samples. Those rejected sequences allow to speculate about new virus types with respect to nucleotide base mutations in the viral sequences. Moreover, this rejection analysis improves model robustness. Last but not least, the presented approach has lower computational complexity compared to methods based on (multiple) sequence alignment. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-021-06018-2.

With the rapid development of network data communication technology, the complexity of the network environment makes the data in the data stream have uncertain characteristics, and a large number of data streams are generated in many fields. Therefore, it is necessary to find an efficient and accurate processing method to process a large amount of data in a computer network. This paper takes the skyline probability calculation and the actual efficiency of the unknown object index structure in the skyline query processing technology on the uncertain data stream as the research object. Based on the skyline query technology, we study an efficient skyline query processing method on the unknown network traffic based on the model SUMG. The method includes two algorithms: dynamic modeling algorithm DMG and skyline query algorithm GST. The DMG algorithm samples the data in the sliding window of the uncertain data stream and establishes a model to convert the data stream into the parameter stream in the uncertain object probability density function, the GST algorithm establishes the R-tree index structure, which is in order to reduce the amount of calculation, it uses the parameter flow of the model. In both methods, the set of local skyline results is first obtained at the distributed node, and the skyline query is performed again on the union of the local skyline results to obtain the global skyline result set. The experimental results show that compared with the skyline query method BNL for unknown network traffic without an index structure, the SUMG method can not only effectively model the link-type unknown object to assist the skyline query, but also effectively prune the uncertain data object and improve the skyline. The distributed skyline query method can cope with the skyline query task on the distributed data stream.

Chronological uncertainty is a hallmark of the paleoenvironmental sciences and geosciences. While many tools have been made available to researchers to quantify age uncertainties suitable for various settings and assumptions, disparate tools and output formats often discourage integrative approaches. In addition, associated tasks like propagating age-model uncertainties to subsequent analyses, and visualizing the results, have received comparatively little attention in the literature and available software. Here, we describe geoChronR, an open-source R package to facilitate these tasks. geoChronR is built around an emerging data standard (Linked PaleoData, or LiPD) and offers access to four popular age-modeling techniques (Bacon, BChron, OxCal, BAM). The output of these models is used to conduct ensemble data analysis, quantifying the impact of chronological uncertainties on common analyses like correlation, regression, principal component, and spectral analyses by repeating the analysis across a large collection of plausible age models. We present five real-world use cases to illustrate how geoChronR may be used to facilitate these tasks, visualize the results in intuitive ways, and store the results for further analysis, promoting transparency and reusability.

Uncertain data objects are objects that can be characterized by either a probability density function (PDF) or with multiple points. Because of existing levels of uncertainty for uncertain data objects, the scatter of this type of objects might be very different than the scatter of certain data objects. Measures of scatter for uncertain objects have not been defined before. In this paper, we define covariance matrix, within scatter matrix, and between scatter matrix as the measures of scatter for uncertain data objects. Also, in this paper, we extend the idea of Fisher linear discriminant analysis for uncertain objects (UFLDA). We also develop kernel Fisher discriminant analysis for uncertain objects (UKFDA). The developed uncertain kernel Fisher discriminants are for two cases: 1) when the uncertain objects are given with PDF and 2) when the uncertain objects are given with multiple points. We compare the performance of our developed uncertain Fisher discriminants (UFLDA and UKFDA) with a few other methods in classification of uncertain data objects through several examples on both simulated and real-world data. We will show in the experiments that our developed uncertain Fisher discriminants outperform other methods in classifying uncertain data objects.

It is widely known that there is a lot of useful information hidden in big data, leading to a new saying that "data is money." Thus, it is prevalent for individuals to mine crucial information for utilization in many real-world applications. In the past, studies have considered frequency. Unfortunately, doing so neglects other aspects, such as utility, interest, or risk. Thus, it is sensible to discover high-utility itemsets (HUIs) in transaction databases while utilizing not only the quantity but also the predefined utility. To find patterns that can represent the supporting transaction, a recent study was conducted to mine high utility-occupancy patterns whose contribution to the utility of the entire transaction is greater than a certain value. Moreover, in realistic applications, patterns may not exist in transactions but be connected to an existence probability. In this paper, a novel algorithm, called High-Utility-Occupancy Pattern Mining in Uncertain databases (UHUOPM), is proposed. The patterns found by the algorithm are called Potential High Utility Occupancy Patterns (PHUOPs). This algorithm divides user preferences into three factors, including support, probability, and utility occupancy. To reduce memory cost and time consumption and to prune the search space in the algorithm as mentioned above, probability-utility-occupancy list (PUO-list) and probability-frequency-utility table (PFU-table) are used, which assist in providing the downward closure property. Furthermore, an original tree structure, called support count tree (SC-tree), is constructed as the search space of the algorithm. Finally, substantial experiments were conducted to evaluate the performance of proposed UHUOPM algorithm on both real-life and synthetic datasets, particularly in terms of effectiveness and efficiency., Comment: Elsevier Information Sciences, 12 figures

In the machine learning and pattern recognition fraternity, uncertain data clustering is an essential job because uncertainty in data makes the clustering process more difficult. Recently, multi-view clustering is gaining more attention towards data miners for certain data because it produces good results compared to grouping based on a single viewpoint. In uncertain data clustering, similarity measure plays an imperative role. However, state-of-the-art similarity measures suffer from several limitations. For example, when two distributions of two uncertain data are heavily overlapped in locations, then Geometric similarity measure alone is not sufficient. On the other hand, similarity measure based on probability distribution is not enough when two uncertain data are not closed to each other or completely separated. In this study, induced kernel distance and Jeffrey-divergence are fused by the degree of overlap concerning each view of a dataset to construct a self-adaptive mixture similarity measure (SAM). The SAM is further used with pairwise co-regularization in multi-view spectral clustering for grouping uncertain data. The proof of convergence of the objective function of the proposed clustering algorithm is also presented in this study. All the experiments are carried out on nine real-world deterministic datasets, three real-life and one synthetic uncertain datasets. Nine real-world deterministic datasets are further converted into uncertain datasets before executing all the clustering algorithms. Experimental results illustrate that the proposed algorithm outperforms nine state-of-the-art methods. The comparison is made using five clustering evaluation metrics. The proposed method is also tested using null hypothesis significance tests.

The theory of three-way decision plays an important role in decision making and knowledge reasoning. However, little attention has been paid to the problem of learning from partially labeled data with three-way decision. In this paper, we propose a three-way co-decision model for partially labeled data. More specifically, the problem of attribute reduction for partially labeled data is first investigated, and two semi-supervised attribute reduction algorithms based on novel confidence discernibility matrix are proposed. Then, a three-way co-decision model is introduced to classify unlabeled data into useful, useless, and uncertain data, and the model is iteratively retrained on the carefully selected useful data to improve its performance. Moreover, we theoretically analyze the effectiveness of the proposed model. The experimental results conducted on UCI data sets demonstrate that the proposed model is promising, and even compares favourably with the single supervised classifier trained on all training data with true labels.

The internet of things provides a convenient life for all of us. Physical devices with locational systems connected to the internet can provide navigation and recommendation services in cooperation with the Location Based Services (LBS). The nearest neighbor query is the most common query type in the LBS research area. On the other hand, due to privacy protection or inaccuracies in measurement instructions, data uncertainties are typical in the IoT environment. This uncertainty will affect the results of nearest neighbor queries. The existence of plane obstacles also brings the challenge to the spatial data query. In this paper, the obstacles and data uncertainties in continuous nearest query are studied. We also designed a pruning strategy that significantly reduced the number of objects to be calculated. In addition, we propose a concept of safe region and an algorithm to generate a safe region. We also designed an index method for saving safe regions. The experimental results show that our approach outperforms the state-of-the-art one in efficiency and scalability.

Fuzzy Cognitive Map is recognized as an important model in soft computing. The Fuzzy Cognitive Map theory studies often treat uncertainty modeling and dynamic modeling separately. So far, there is no single Fuzzy Cognitive Map that can deal with uncertain data and dynamic environments simultaneously. This paper proposed an environment model to describe the link between changing weights and the dynamic environment. As the extensions of the classic Fuzzy Grey Cognitive Map, two dynamic models were designed and implemented in this work: Dynamic Fuzzy Grey Cognitive Map and Dynamic Fuzzy General Grey Map. In this work, we also analyze the characteristics of the two models and evaluate their performance using industrial control problems. The results showed that the proposed models could well handle uncertain data in dynamic environments.

Learning with label proportions (LLP), which only provides the unlabeled instances in the bag and the bag’s label proportion, has been widely studied recently. However, most of the existing LLP methods do not consider the knowledge transfer from the source task to the target task. In addition, in the process of the collecting data, the data may be corrupted by noise, and this always leads to the uncertain data in its representation. This paper proposes a new transfer learning-based approach for the problem of learning with label proportions, which is called TL-LLP in brief, to transfer knowledge from the source task to the target task where both the source and target tasks contain uncertain data. We first formulate the objective model to deal with transfer learning and uncertain data for the label proportions problem at the same time. We then propose an iterative framework to solve the proposed objective model and obtain the accurate classifier for the target task. Extensive experiments have shown that the proposed TL-LLP method can obtain better performance and is less sensitive to noise compared with the existing LLP methods.

The fuzzy rough set (FRS) acts as a powerful mathematical tool to deal with uncertain data, and it has many applications in feature selection, dimensionality reduction and classification. The fuzzy rough set based on robust nearest neighbor (FRS-RNN) is one of the vital classifiers which has been successfully applied to handle real-valued datasets. From the literature, it is very clearly evident that no research attempt has been made on FRS-RNN to text document classification. Generally, the document classification process consists of two crucial phases, namely feature extraction and classifier model construction. Mainly TF-IDF and convolutional neural network (CNN)-based techniques are used for efficient feature extraction. The CNN provides the best feature engineering through effective preprocessing the documents for better representation using pre-trained word embedding. In this paper, we proposed a modified CNN structure for both text document classification and feature extraction. Then, both FRS and FRS-RNN have been implemented for text document classification on the benchmark datasets like 20 Newsgroup and Reuter-21578 using both TF-IDF and modified CNN-based feature extraction techniques. The classification performance of the FRS, CNN and FRS-RNN is evaluated and compared using well-defined metrics like accuracy, precision, recall and F1-measure. Finally, the classification performance of FRS-RNN is compared with state-of-the-art traditional classification models such as SVM, KNN, Naive Bayes, DNN, CNN and RNN and with some recently developed classification models. The experimental results followed by empirical evaluation show that the proposed FRS-RNN outperforms all the aforementioned classification models.

Bayesian inference is a technique of statistical inference which uses the Bayes’ theorem to update the probability distribution as new observed data are available. Uncertain variables are a tool of...

Modern Internet of Things (IoT) applications generate massive amounts of data, much of it in the form of objects/items of readings, events, and log entries. Specifically, most of the objects in these IoT data contain rich embedded information (e.g., frequency and uncertainty) and different level of importance (e.g., unit utility of items, interestingness, cost, risk, or weight). Many existing approaches in data mining and analytics have limitations such as only the binary attribute is considered within a transaction, as well as all the objects/items having equal weights or importance. To solve these drawbacks, a novel utility-driven data analytics algorithm named HUPNU is presented, to extract High-Utility patterns by considering both Positive and Negative unit utilities from Uncertain data. The qualified high-utility patterns can be effectively discovered for risk prediction, manufacturing management, decision-making, among others. By using the developed vertical Probability-Utility list with the Positive-and-Negative utilities structure, as well as several effective pruning strategies. Experiments showed that the developed HUPNU approach performed great in mining the qualified patterns efficiently and effectively., Comment: Under review in IEEE Internet of Things Journal since 2018, 11 pages

Everyone needs to make decisions under uncertainty and with limited resources, e.g., an investor who is building a stock portfolio subject to an investment budget and a bounded risk tolerance. Doing this with current technology is hard. There is a disconnect between software tools for data management, stochastic predictive modeling (e.g., simulation of future stock prices), and optimization; this leads to cumbersome analytical workflows. Moreover, current methods do not scale. To handle a broad class of uncertainty models, analysts approximate the original stochastic optimization problem by a large deterministic optimization problem that incorporates many "scenarios", i.e., sample realizations of the uncertain data values. For large problems, a huge number of scenarios is required, often causing the solver to fail. We demonstrate sPaQLTooLs, a system for in-database specification and scalable solution of constrained optimization problems. The key ingredients are (i) a database-oriented specification of constrained stochastic optimization problems as "stochastic package queries" (SPQs), (ii) use of a Monte Carlo database to incorporate stochastic predictive models, and (iii) a new SummarySearch algorithm for scalably solving SPQs with approximation guarantees. In this demonstration, the attendees will experience first-hand the difficulty of manually constructing feasible and high-quality portfolios, using real-world stock market data. We will then demonstrate how SummarySearch can easily and efficiently help them find very good portfolios, while being orders of magnitude faster than prior methods.

Hierarchical classification (HC) is effective when categories are organized hierarchically. However, the blocking problem makes the effect of hierarchical classification greatly reduced. Blocking means that samples are easily getting misclassified in high-level classifiers so that the samples are blocked at the high-level of the hierarchy. This issue is caused by the inconsistency between the artificially defined hierarchy and the actual hierarchy of the raw data. Another issue is that it is flippant to strictly process data following the hierarchy. Therefore, special treatment is required for some uncertain data. To address the first issue, we learn category relationships and modify the hierarchy. To address the second issue, we introduce three-way decisions (3WD) to targetedly deal with the ambiguous data. We extend original studies and propose two HC models based on 3WD, collectively referred to as TriHC, for carefully modifying the hierarchy to alleviate the blocking problem. The proposed TriHC model learns new category hierarchies by the following three steps: (1) mining category relations; (2) modifying category hierarchies according to the latent category relations; and (3) using 3WD to divide observed objects into three regions: positive region, boundary region, and negative region, and making decisions based on different strategies. Specifically, based on different category relation mining methods, there are two versions of TriHC, cross-level blocking priori knowledge based TriHC (CLPK-TriHC) and expert classifier based TriHC (EC-TriHC). The CLPK-TriHC model defines a cross-level blocking distribution matrix to mine the category relations between the higher and lower levels. To better exploit category hierarchical relations, the EC-TriHC model builds expert classifiers using topic model to learn latent category topics. Experimental results validate that the proposed methods can simultaneously reduce the blocking and improve the classification accuracy.

In recent years, numerous applications have been continuously generating large amounts of uncertain data. The advanced analysis queries such as skyline operators are essential topics to extract interesting objects from the vast uncertain dataset. Recently, the MapReduce system has been widely used in the area of big data analysis. Although the probabilistic skyline query is not decomposable, it does not make sense to implement the probabilistic skyline query in the MapReduce framework. This paper proposes an effective parallel method called parallel computation of probabilistic skyline query (PCPS) that can measure the probabilistic skyline set in one MapReduce computation pass. The proposed method takes into account the critical sections and detects data with a high probability of existence through a proposed smart sampling algorithm. PCPS implements a new approach to the fair allocation of input data. The experimental results indicate that our proposed approach can not only reduce the processing time of the probabilistic skyline queries, but also achieve fair precision with varying dimensionality degrees.

Due to the rapid development of Medical IoT recently, how to effectively apply these huge amounts of IoT data to enhance the reliability of the clinical decision making has become an increasing issue in the medical field. These data usually comprise high-complicated features with tremendous volume, and it implies that the simple inference models may less powerful to be practiced. In deep learning, multilayer perceptron (MLP) is a kind of feed-forward artificial neural network, and it is one of the high-performance methods about stochastic scheme, fitness approximation, and regression analysis. To process these high uncertain data, the proposed work based on MLP structure in particular integrates the boosting scheme and dimension-reduction process. In this proposed work, the advanced ReLU-based activation function is used. Also, the weight initialization is applied to improve the stable prediction and convergence. After the improved dimension-reduction process is introduced, the proposed method can effectively learn the hidden information from the reformative data and the precise labels also can be recognized by stacking a small amount of neural network layers with paying few extra cost. The proposed work shows a possible path of embedding dimension reduction in deep learning structure with minor price. In addition to the prediction issue, the proposed method can also be applied to assess risk and forecast trend among different information systems.

Purpose In recent years, determining the effective and suitable supplier in the supply chain management (SCM) has become a key strategic consideration to the success of any manufacturing organization in terms of business intelligence (BI), as many quantitative and qualitative critical factors are measured from big data. In today’s competitive business scenario, the main purpose of this study is to determine suitable and sustainable suppliers during supplier selection process is to reduce the risk of investment along with maximize overall value to the customer and develop closeness and long-term relationships between customers and suppliers to build a resilient SCM to mitigate uncertainty for automotive organizations. Design/methodology/approach As these types of decisions generally involve more than a few criteria and often necessary to compromise among possibly conflicting factors, the multiple-criteria decision-making becomes a useful approach to solve this kind of problem. Considering both tangible and intangible criteria, the aim of this paper is the presentation of a new integrated fuzzy analytic hierarchy process and fuzzy additive ratio assessment method with fuzzy entropy using linguistic values to solve the supplier selection problem to build the resilient SCM under uncertain data. Fuzzy entropy is used to obtain the entropy weights of the criteria. Findings Organizations gather massive amounts of information known as BD on the basis of historical records of uncertainties from several internal and external sources to manage uncertainty to improve the overall performance of organizations using BI strategy for analyzing and making effective decision to support the managements of automotive manufacturing organizations in an information system. Research limitations/implications Although this study tries to represent a full analysis on suitable and resilient global supplier selection under various types of uncertainty, still there are some improvements that can be made in the future by developing a more refined and more sophisticated approach to further enhance the performance of the proposed scheme to calculate overall rating scores of the alternatives. Originality/value The novelty of this paper is to propose a framework of BI in SCM to determine a suitable and resilient global supplier where all the meaningful information, relevant knowledge and visualization retrieved by analyzing the huge and complex set of data or data streams, i.e. BD based on decision-making, to develop any manufacturing organizational performance worldwide.

Coronavirus disease (COVID-19) has spread for over a year and affected many aspects, including the food supply chain. One of the ways COVID-19 has impacted the food supply chain is the food production capacity reduction. It is necessary to develop the optimum food supply chain strategy by determining the optimum food hub location and food network to maintain food security which robust against disruptions and uncertainties. In this study, Robust Optimization (RO) is applied to handle the uncertainties. Nevertheless, the actual uncertain data might be hard to be collected or even unavailable at the moment. Therefore, an innovative framework is proposed to integrate RO with Agent-Based Modelling (ABM). ABM is used to simulate the upstream actor of the food supply chain and predict the uncertain food production capacity, which RO later handles. Particularly, this study focused on rice supply chain. The result shows that the framework is able to handle the uncertain rice supply chain problem, in which the actual uncertain data might be unavailable, and give the robust optimum food hub location and food network. The food hub location and food network are obtained by solving the Robust Counterpart (RC) model with respect to the uncertainty set obtained from the ABM simulation result., Food supply chain; COVID-19; Robust Optimization; Agent-Based Modelling; Uncertainty; Simulation

In this chapter, we provide a brief overview of recent research works based on fuzzy set theory that aim to make data management systems more flexible. Three aspects are considered: (i) preference queries to classical databases, (ii) modeling and handling of uncertain data, and (iii) cooperative query-answering techniques.

Technological advancements have led to easy and rapid generation and collection of huge amounts of varieties of data from of wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by data analytics. Discovered information and knowledge may help to build a smart city and then a smart world. In this paper, we focus on making good fusion of conceptual modelling and knowledge graphs to capture essential data about public transportation (e.g., buses). Specifically, our conceptual model and knowledge graph capture information regarding bus arrival and departure. Some of the captured data can be uncertain (e.g., timestamp, GPS locations of buses) due to the limitations of the measuring devices and methods to collect the data. Nonetheless, the models are helpful in smart city big data analytics of these public transportation data. The discovered knowledge produces insights to users (e.g., city planners, policy makers), which in turn help them to build a smart city.

Technological advancements have led to easy and rapid generation and collection of huge volumes of varieties of data from of wide ranges of rich data sources. These big data may be of different levels of veracity, including precise data and imprecise or uncertain data. Embedded in the data are valuable information and useful knowledge that can be discovered by data mining. Discovered information and knowledge may help to build a smart world. In this paper, we present a solution for scalable mining of big data. In particular, we focus on scalable mining of huge volumes of temporal coronavirus disease 2019 (COVID-19) data at different granularity levels. Since its outbreak, there have been millions of COVID-19 cases worldwide. These are huge volumes of data, and new cases have been reported every day. Embedded in these COVID-19 data is implicit, previously unknown and potentially useful information and knowledge, which can be discovered by data mining for social good. Analyzing and mining these data helps users (e.g., researchers, civilian) to get better understanding of the disease, and thus take an active role in fighting, controlling, and/or combating the disease. Evaluation results on real-life COVID-19 data show the benefits of our solution in scalable mining of big data.

Like the case of any software, Web Services (WSs) developers could introduce anti-patterns due to the lack of experience and badly-planned changes. During the last decade, search-based approaches have shown their outperformance over other approaches mainly thanks to their global search ability. Unfortunately, these approaches do not consider the uncertainty of class labels. In fact, two experts could be uncertain about the smelliness of a particular WS interface but also about the smell type. Currently, existing works reject uncertain data that correspond to WSs interfaces with doubtful labels. Motivated by this observation and the good performance of the possibilistic K-NN classifier in handling uncertain data, we propose a new evolutionary detection approach, named Web Services Anti-patterns Detection and Identification using Possibilistic Optimized K-NNs (WS-ADIPOK), which can cope with the uncertainty based on the Possibility Theory. The obtained experimental results reveal the merits of our proposal regarding four relevant state-of-the-art approaches.

Outsourcing is the transfer of a business process that has been traditionally operated and managed internally to external service providers. In order to reduce costs and improve their core competitiveness, many companies tend to choose the outsourcing of some services. The problem of this type belongs to multicriteria decision making. The imprecision in this problem may arise from the nature of the characteristics of the candidates for the outsourcing service providers, which can be unavailable or indeterminate. It may also be derived from the inability of the experts to formulate a precise evaluation. Intuitionistic fuzzy sets (IFSs) are the stronger tool for dealing with uncertain data than fuzzy ones because, unlike them, they have a degree of hesitancy.

Network optimization is a branch of mathematical programming studying how to plan, optimize, and manage the network to improve its performance, which has been widely investigated and practiced in t...

Uncertain data pervades many fields, including environmental monitoring, the monitoring of animal migrations, and urban warfare. Such uncertain data collected by field devices, such as Internet of Things (IoT) and Internet of Battlefield Things (IoBT) devices, may also be encrypted and outsourced to an untrustworthy third party for storage and data sharing such as a cloud server. However, the properties of uncertain data and the complication of operating over encrypted data make the searching schemes more ineffective. In this article, we design an efficient and safe ${K}$ nearest neighbor (KNN) query scheme for uncertain data stored in semi-trusted cloud servers. We apply the modified homomorphic encryption, which requires two servers to interact and encrypt the uncertain data, and we use the authorized rank method to compute KNN. We protect the security of the data while simultaneously improving the query efficiency. Our detailed security analysis show that our scheme can realize the goal of concealing both the access and the search patterns. Comprehensive experiments are conducted to demonstrate the scheme’s performance.

The evidential K nearest neighbor classifier is based on discounting evidence from learning instances in a neighborhood of the pattern to be classified. To adapt the method to partially supervised data, we propose to replace the classical discounting operation by contextual discounting, a more complex operation based on as many discount rates as classes. The parameters of the method are tuned by maximizing the evidential likelihood, an extension of the likelihood function based on uncertain data. The resulting classifier is shown to outperform alternative methods in partially supervised learning tasks.

This study focuses on developing an innovative multicriteria group decision-making (MCGDM) method to handle practical problems based on information description, information fusion and information measure. Given that various uncertain data exist in the process of managing problems, uncertain Z-numbers are introduced to represent evaluation information and measure information reliability simultaneously. Processing uncertain information requires the effective support of reliable tools; then, cloud models can be used to address uncertain Z-number information. First, uncertain Z-numbers are converted, and a novel concept of Z-trapezium-normal clouds (ZTNCs) is introduced. Next, the operations, power aggregation operators, and distance and likelihood measures of ZTNCs are defined. Subsequently, the likelihood power aggregation operator and the priority vector of the likelihood matrix are proposed. Moreover, a new MCGDM method is developed by combining the two kinds of proposed power aggregation operators with the likelihood measure. Finally, an illustrative example of new energy evaluation is provided to demonstrate the developed method, and its feasibility and validity are further verified by a comparative analysis with other existing methods.

In this paper a methodology is presented to update estimated level of corrosion damage using routine short-term health monitoring measurement data. The case study bridge is a reinforced concrete (RC) slab. It is subject to traffic loading from a weigh-in-motion database and time-dependent deterioration is modelled through corrosion. Different damage indicators based on different virtual sensor measurements (strain, deflection and rotation) are investigated. A method of comparing the performance of the damage indicators is presented and their data is used to achieve a Bayesian update of prior estimates of bridge reinforcement corrosion. It is shown how, with few and uncertain data, the estimation of deterioration level can be significantly improved, particularly with damage indicators based on rotation.