Search

With the rapidly expansion of big data in all domains, it has become a major research topic to improve the performance of mining frequent patterns in massive uncertain datasets in recent years. Most conventional frequent pattern mining approaches take expect, probability, or weight as one single factor of item support, and algorithms that consider both probability and weight are unable to balance execution efficiency under the circumstances of big data. Therefore, we propose a distributed frequent itemset mining algorithm for uncertain data: Dfimud. Firstly, Dfimud calculates the maximum probability weight value of 1-items and prunes the items whose value is less than the given threshold. Secondly, to reduce the times of scanning the datasets, a distributed Dfimud-tree structure inspired by FP-Tree is designed to mine frequent patterns. Finally, experiments on publicly available UCI datasets demonstrate that Dfimud achieves more optimal results than other related approaches across various metrics. In addition, the empirical study also shows that Dfimud has good scalability. [ABSTRACT FROM AUTHOR]

In recent decades, frequent pattern mining has become popular in real time computing because it helps users extract meaningful business or technical trends over uncertain data streams. Generally, mainstream frequent pattern mining approaches can accurately explore frequent patterns whether the approaches are used in expected support or probabilistic frequentness mechanisms. However, whichever are implemented, they all have time issues when extracting frequent patterns if there exists a volume of data tuples in each time instance. To explore frequent patterns over uncertain data streams efficiently, we propose a predicted approach for forecasting frequent patterns with hidden Markov models. The model consists of a construction phase and a testing phase. In the former phase, the hidden Markov model is constructed by the history of past data, and in the latter phase, the results of evaluations and adjustments of transition probabilities occur. By constructing models of patterns, frequent patterns can be easily obtained. Experimental results show the proposed approach can save time cost when exploring frequent patterns and provide approximate 72% average accuracy in 50 sequential predictions; thereby, our approach can efficiently and accurately predict frequent patterns over uncertain data streams. [ABSTRACT FROM AUTHOR]

Universal generating function (UGF) technology is the basic approach to reliability evaluation of multi-state systems. For products with high reliability and long lifetime in practical engineering, their system structure is usually uncertain and the external working condition is extremely complex. Thus, accurate failure or degradation data of components are difficult to define. Data are often characterized by types of randomness, probability, grey, fuzzy and other uncertain numbers. Based on interval grey number, the concept of generalized uncertain number is proposed. In this paper, we first unify arbitrary type of uncertain numbers, propose the definition of generalized standard uncertain number and study algorithm such as addition, subtraction, multiplication and division between them. Based on generalized standard uncertain number, generalized universal generating function, which is an extension of fuzzy UGF, is proposed. Several commonly used generalized universal generating operators are discussed with system connection structure between components. According to the relationship between output performance and system demand level, reliability indexes such as availability, expected output performance and mean performance deficiency are evaluated. Comparisons in a real case show the effectiveness of the proposed approach. Therefore, relevant results can strengthen the theoretical research depth of uncertain reliability and definitely expand the application scope of fuzzy universal generating function. [ABSTRACT FROM AUTHOR]

Let N be the line-set and M be the column-set of a matrix { a i j } , such that a i j = 1 if line i ∈ N is covered by column j ∈ M , or a i j = 0 otherwise. Besides, let b j ≥ 0 be the benefit associated with a column j ∈ M . Given a constant T < | M | , the NP-Hard Maximal Covering Location Problem (MCLP) consists in finding a subset X ⊆ M with the maximum sum of benefits, such that | X | ≤ T and every line in N is covered by at least one column in X . In this study, we investigate the min-max regret Maximal Covering Location Problem, a robust counterpart of MCLP, where the benefit of each column is uncertain and modeled as an interval data. The objective is to find a robust solution that minimizes the maximal regret over all possible combinations of values for the columns benefit. This problem has applications in disaster relief. We propose a MILP formulation, an exact and 2-approximation algorithms, and test them using classical instances from the literature. [ABSTRACT FROM AUTHOR]

High volumes of wide varieties of valuable data of different veracity (e.g., imprecise and uncertain data) can be easily generated or collected at a high velocity for various knowledge-based and intelligent information & engineering systems in many real-life situations. Embedded in these big data is valuable knowledge and useful information, which can be discovered by data science solutions. As a popular data science task, frequent pattern mining aims to discover implicit, previously unknown and potentially useful information and valuable knowledge in terms of sets of frequently co-occurring items. Many of the existing frequent pattern mining algorithms use a transaction-centric mining approach to find frequent patterns from precise data. However, there are situations in which an item-centric mining approach is more appropriate, and there are also situations in which data are imprecise and uncertain. In this article, we present an item-centric algorithm for mining frequent patterns from big uncertain data. Evaluation results show the effectiveness of our algorithm in item-centric mining of frequent patterns from big uncertain data. [ABSTRACT FROM AUTHOR]

Quantitative demonstrating of organic frameworks has turned into an essential computational methodology in the configuration of novel and investigation of existing natural frameworks. Be that as it may, active information that portrays the framework's elements should be known keeping in mind the end goal to get pertinent results with the routine displaying strategies. This information is frequently robust or even difficult to get. Here, we exhibit a model of quantitative fuzzy rational demonstrating approach that can adapt to obscure motor information and hence deliver applicable results despite the fact that dynamic information is fragmented or just dubiously characterized. Besides, the methodology can be utilized as a part of the blend with the current cutting edge quantitative demonstrating strategies just in specific parts of the framework, i.e., where the data are absent. The contextual analysis of the methodology suggested in this paper is performed on the model of nine-quality genes. We propose a kind of FPN model in light of fuzzy sets to manage the quantitative modeling of biological systems. The tests of our model appear that the model is practical and entirely powerful for information impersonation and thinking of fuzzy expert frameworks. [ABSTRACT FROM AUTHOR]

Indexing and query processing is an emerging research field in spatio - temporal data. Most of the real-time applications such as location based services, fleet management, traffic prediction and radio frequency identification and sensor networks are based on spatiotemporal indexing and query processing. All the indexing and query processing applications is any one of the forms, such as spatio index access and supporting queries or spatio-temporal indexing method and support query or temporal dimension, while in spatial data it is considered as the second priority. In this paper, give the survey of the various uncertain indexing and query processing techniques. Most of the existing survey works on spatio-temporal are based on indexing methods and query processing, but presented separately. Both the indexing and querying are related, hence state - of - art of both the indexing and query processing techniques are considered together. This paper gives the details of spatio-temporal data classification, various types of indexing methods, query processing, application areas and research direction of spatio-temporal indexing and query processing. [ABSTRACT FROM AUTHOR]

Probabilistic Frequent Itemset (PFI) mining in uncertain transaction database has attracted much attention from the data mining and database communities. But there are many probabilistic frequent itemsets having the same value of frequent probability when the uncertain transaction database adopts the tuple uncertainty model. Then, substantial redundant rules will be generated if we use these PFls to discover association rules. To address this problem, we study discovering probabilistic frequent closed itemsets (PfCI) in uncertain transaction database with tuple uncertainty. In this paper, we introduce a new definition of probabilistic frequent closed itemset, differing from the existent definition, which has the same power but less number of probabilistic frequent itemsets. We prove that probabilistic frequent closed itemset defined by us is the lossless compression of probabilistic frequent itemset in uncertain database with tuple uncertainty. Then, we present an efficient mining method to solve the PFCI Mining problem. This method can discover all the PFCIs without mining all the PFls. To reduce the search space and computation, we further design several pruning methods. Experimental evaluation verifies the effectiveness and efficiency of our algorithm. [ABSTRACT FROM AUTHOR]

Most of the real-world data is gathered locally, organized regionally leading to distributed environment. The analysis of such data will assist decision makers for promoting their business. Most of the data mining strategies are multi-pass techniques employed for mining and discovering the knowledge. Hence, the paper focuses on developing two pass data mining constructs to handle uncertain data. One of the simplest and feasible item-pattern representations is transactional tree. The developed methodology for constructing transactional trees is studied in detail and apt technological solutions is proposed. It is proposed to construct transactional tree from uncertain data which is usually associated with existential probabilities and to establish distributed computing environment for developing transactional trees. This paper aims at bringing out the knowledge valid for a given location through which it is expected to derive global knowledge. [ABSTRACT FROM AUTHOR]

With the evolution of technology, the ways to acquire data and the applications of data are more diverse. As data volume con- tinuously grows, the data quality may not be high as usual. The data can be defected, imprecise or inaccurate due to the process of data acquiring. Recently, the skyline query is widely used in data analysis to derive the results that meets more than one spe- cific condition simultaneously. For example, the forest monitoring system, which collects the temperature and humidity of the surrounding environment with sensors, to monitor the disasters. Using the skyline query, the zones of potential fire hazards can be found in time, where the temperature is high and the humidity is low. In the mentioned application, the derived data change with time. We refer to such data as data streams. The constant change and uncertainty of data make the query process difficult and need more computations. Thus, how to have an effective skyline query process in terms of time and space over uncertain data streams becomes crucial. In this paper, we discuss this problem and propose an effective approach, Efficient Probabilistic Skyline Update (EPSU), using a new data structure by augmenting the R-tree structure. The relevant algorithms are analyzed and discussed. Last, we perform the simulated experiments extensively with synthetic data to validate the EPSU approach. The results show that EPSU can effectively compute the probabilistic skyline query in terms of the time and space and outperforms the existing ones. [ABSTRACT FROM AUTHOR]

With the evolution of technology, the ways to acquire data and the applications of data are more diverse. As data volume con- tinuously grows, the data quality may not be high as usual. The data can be defected, imprecise or inaccurate due to the process of data acquiring. Recently, the skyline query is widely used in data analysis to derive the results that meets more than one spe- cific condition simultaneously. For example, the forest monitoring system, which collects the temperature and humidity of the surrounding environment with sensors, to monitor the disasters. Using the skyline query, the zones of potential fire hazards can be found in time, where the temperature is high and the humidity is low. In the mentioned application, the derived data change with time. We refer to such data as data streams. The constant change and uncertainty of data make the query process difficult and need more computations. Thus, how to have an effective skyline query process in terms of time and space over uncertain data streams becomes crucial. In this paper, we discuss this problem and propose an effective approach, Efficient Probabilistic Skyline Update (EPSU), using a new data structure by augmenting the R-tree structure. The relevant algorithms are analyzed and discussed. Last, we perform the simulated experiments extensively with synthetic data to validate the EPSU approach. The results show that EPSU can effectively compute the probabilistic skyline query in terms of the time and space and outperforms the existing ones. [ABSTRACT FROM AUTHOR]

Processing lineages (also called provenances) over uncertain data consists in tracing the origin of uncertainty based on the process of data production and evolution. In this paper, we focus on the representation and processing of lineages over uncertain data, where we adopt Bayesian network (BN), one of the popular and important probabilistic graphical models (PGMs), as the framework of uncertainty representation and inferences. Starting from the lineage expressed as Boolean formulae for SPJ (Selection–Projection–Join) queries over uncertain data, we propose a method to transform the lineage expression into directed acyclic graphs (DAGs) equivalently. Specifically, we discuss the corresponding probabilistic semantics and properties to guarantee that the graphical model can support effective probabilistic inferences in lineage processing theoretically. Then, we propose the function-based method to compute the conditional probability table (CPT) for each node in the DAG. The BN for representing lineage expressions over uncertain data, called lineage BN and abbreviated as LBN, can be constructed while generally suitable for both safe and unsafe query plans. Therefore, we give the variable-elimination-based algorithm for LBN's exact inferences to obtain the probabilities of query results, called LBN-based query processing. Then, we focus on obtaining the probabilities of inputs or intermediate tuples conditioned on query results, called LBN-based inference query processing, and give the Gibbs-sampling-based algorithm for LBN's approximate inferences. Experimental results show the efficiency and effectiveness of our methods. [ABSTRACT FROM AUTHOR]

Knowledge discovery and data mining generally discovers implicit, previously unknown, and useful knowledge from data. As one of the popular knowledge discovery and data mining tasks, frequent itemset mining, in particular, discovers knowledge in the form of sets of frequently co-occurring items, events, or objects. On the one hand, in many real-life applications, users mine frequent patterns from traditional databases of precise data, in which users know certainly the presence of items in transactions. On the other hand, in many other real-life applications, users mine frequent itemsets from probabilistic sets of uncertain data, in which users are uncertain about the likelihood of the presence of items in transactions. Each item in these probabilistic sets of uncertain data is often associated with an existential probability expressing the likelihood of its presence in that transaction. To mine frequent itemsets from these probabilistic datasets, many existing algorithms capture lots of information to compute expected support. To reduce the amount of space required, algorithms capture some but not all information in computing or approximating expected support. The tradeoff is that the upper bounds to expected support may not be tight. In this paper, we examine several upper bounds and recommend to the user which ones consume less space while providing good approximation to expected support of frequent itemsets in mining probabilistic sets of uncertain data. [ABSTRACT FROM AUTHOR]

Uncertain is inherent in moving object trajectories due to measurement errors or time-discretized sampling. Unfortunately, most previous research on trajectory pattern mining did not consider the uncertainty of trajectory data. This paper focuses on the uncertain group pattern mining, which is to find the moving objects that travel together. A novel concept, uncertain group pattern, is proposed, and then a two-step approach is introduced to deal with it. In the first step, the uncertain objects' similarities are computed according to their expected distances at each timestamp, and then the objects are clustered according to their spatial proximity. In the second step, a new algorithm to efficiently mining the uncertain group patterns is designed which captures the moving objects that move within the same clusters for certain timestamps that are possibly nonconsecutive. However the search space of group pattern is huge. In order to improve the mining efficiency, some pruning strategies are proposed to greatly reduce the search space. Finally, the effectiveness of the proposed concepts and the efficiency of the approaches are validated by extensive experiments based on both real and synthetic trajectory datasets. [ABSTRACT FROM AUTHOR]

In this paper we attempt to make a theoretical comparison between fuzzy sets and vague sets in processing uncertain queries. We have designed an architecture to process uncertain i.e. fuzzy or vague queries. In the architecture we have presented an algorithm to find the membership value that generates the fuzzy or vague representation of the attributes with respect to the given uncertain query. Next, a similarity measure is used to get each tuples similarity value with the uncertain query for both fuzzy and vague sets. Finally, a decision maker will supply a threshold or a-cut value based on which a corresponding SQL statement is generated for the given uncertain query. This SQL retrieves different result sets from the database for fuzzy or vague data. It has been shown with examples that vague sets give more accurate result in comparison with fuzzy sets for any uncertain query. [ABSTRACT FROM AUTHOR]

To remain competitive and to provide timely and accurate services, the decision maker or system analyst view the reliability and maintainability issues as a part of corporate quest to improve quality of the products/processes and services delivered. But the collected or available data available from the historical records are mostly uncertain, limited and imprecise in nature. Thus in such situation(s), it is difficult, if not impossible, to analyze the behavior and performance of the system up to desired degree of accuracy. Thus attempt has been made by the author for analyzing the behavior and performance of the system using Vague Lambda–Tau methodology. Intuitionistic fuzzy set theory has been used for representing the classical(crisp) data into triangular fuzzy numbers because intuitionistic sets are characterized by a truth membership function and false membership functions (non-membership functions) so that sum of both values is less than 1. Sensitivity analysis has also been performed and the effects on system mean time between failures are addressed. The performance analysis of the system has been investigated through a composite measure of reliability, availability and maintainability (RAM) called RAM-Index which explore the effect of failure/repair rates on system performance. Based on their performance, the most critical component of the system has been investigated. The methodology improves the shortcomings of the existing probabilistic approaches and gives a better understanding of the system behavior through its graphical representation. The pulping unit of a paper mill situated in a northern part of India, producing approximately 200 ton of paper per day, has been considered to demonstrate the proposed approach. The results obtained by proposed approach are compared with the existing fuzzy Lambda–Tau and the crisp methodologies. [ABSTRACT FROM AUTHOR]

Due to advances in technology, high volumes of valuable data can be collected and transmitted at high velocity in various scientific and engineering applications. Consequently, efficient data mining algorithms are in demand for analyzing these data. For instance, frequent pattern mining discovers implicit, previously unknown, and potentially useful knowledge about relationships among frequently co-occurring items, objects and/or events. While many frequent pattern mining algorithms handle precise data, there are situations in which data are uncertain . In recent years, tree-based algorithms for mining uncertain data have been developed. However, tree structures corresponding to these algorithms can be large. Other tree structures for handling uncertain data may achieve compactness at the expense of loose upper bounds on expected supports. In this paper, we propose (i) a compact tree structure for capturing uncertain data, (ii) a technique for using our tree structure to tighten upper bounds to expected support, and (iii) an algorithm for mining frequent patterns based on our tightened bounds. Experimental results show the benefits of our tightened upper bounds to expected supports in uncertain frequent pattern mining. [ABSTRACT FROM AUTHOR]

Data uncertainty inherently exists in a large number of applications due to factors such as limitations of measuring equipments, update delay, and network bandwidth. Recently, modeling and querying uncertain data have attracted considerable attention from the database community. However, how to perform advanced analysis on uncertain data remains an interesting question. In this paper, we focus on the execution of skyline computation over uncertain moving objects. We propose a novel probabilistic skyline model where an uncertain object may take a probability to be in the skyline at a certain time point, therefore a p-t-skyline contains those moving objects whose skyline probabilities are at least p at time point t. Computing probabilistic skyline over a large number of uncertain moving objects is a daunting task in practice. In order to efficiently compute the probabilistic skyline query, we propose a discrete-and-conquer strategy, which follows the sampling-bounding-pruning-refining procedure. To further reduce the skyline computation cost, we propose an enhanced framework that is based on a multi dimensional indexing structure combined with the discrete-and-conquer strategy. Through extensive experiments with synthetic datasets, we show that the framework can efficiently support skyline queries over uncertain moving object and is scalable on large data sets. [ABSTRACT FROM AUTHOR]

During the year 1982, rough set theory was introduced by Z Pawlak in order to deal with uncertain data. Rough set theory relies only on the available data and attributes to analyze features as well as to generate classification rules without any additional information. Here we have used rough set theory to find out reduct and core of the call center data by reducing the superfluous information, and then provided an algorithm based upon the hidden pattern in data which will adequately increase the efficiency of the organization. [ABSTRACT FROM AUTHOR]

SOPDS is a kind of skyline query algorithm over probabilistic data stream. Based on grid index, a set of heuristic rules like probability bounds, progressive refinement, pre-elimination and selective compensation are developed to improve the comprehensive performance of SOPDS on both CPU overhead and memory consumption. Through the analysis of the dominance relationship between uncertain objects, more effective filtering strategy and object identity decision rule are added to SOPDS. And SOPDS is improved to a novel algorithm, ISOPDS. The experimental results show that ISOPDS could reduce the response time of skyline query effectively. [ABSTRACT FROM AUTHOR]