Uncertain Big Data Research Articles

Fast classification algorithm of uncertain big data stream based on distributed limit learning machine

Fast classification algorithm of uncertain big data stream based on distributed limit learning machine

Quantum Fuzzy Regression Model for Uncertain Environment

In the era of big data, traditional regression models cannot deal with uncertain big data efficiently and accurately. In order to make up for this deficiency, this paper proposes a quantum fuzzy regression model, which uses fuzzy theory to describe the uncertainty in big data sets and uses quantum computing to exponentially improve the efficiency of data set preprocessing and parameter estimation. In this paper, data envelopment analysis (DEA) is used to calculate the degree of importance of each data point. Meanwhile, Harrow, Hassidim and Lloyd (HHL) algorithm and quantum swap circuits are used to improve the efficiency of high-dimensional data matrix calculation. The application of the quantum fuzzy regression model to small-scale financial data proves that its accuracy is greatly improved compared with the quantum regression model. Moreover, due to the introduction of quantum computing, the speed of dealing with high-dimensional data matrix has an exponential improvement compared with the fuzzy regression model. The quantum fuzzy regression model proposed in this paper combines the advantages of fuzzy theory and quantum computing which can efficiently calculate high-dimensional data matrix and complete parameter estimation using quantum computing while retaining the uncertainty in big data. Thus, it is a new model for efficient and accurate big data processing in uncertain environments.

A secure technique for unstructured big data using clustering method

Analyzing the multi-dimensional data in faster way is an important and basic aspect in any clustering mechanism. At the same time the clustering mechanism should provide a security structure to protect data loss by preventing distinguish security attacks and transmission errors. Also, to control the data size, security overheads and data loss due to data and time overheads during clustering of unstructured and uncertain big data, a data compression system is also needed. Among the various researches, the current literature fails to suggest any integrated technique to solve these issues in a combinatorial way. Henceforth, this research provides a new compact clustering technique which prevents data loss by including SDES encryption technique to protect security attacks, a new error control scheme to address any number of transmission errors and Huffman compression to control the data size and extra security overheads. Apart from that, the faster execution of proposed integrated technique reduces the time overhead. The experimental result shows, it offers higher data integrity by producing lower percentage of Information Loss, higher SNR and compression ratio. Furthermore, the capacity to produce higher Throughputs and low Cyclomatic Complexity shows its time efficiencies.

Efficient monochromatic and bichromatic probabilistic reverse top-k query processing for uncertain big data

There has been an increasing growth in numerous applications that naturally generate large volumes of uncertain data. By the advent of such applications, the support of advanced analysis query processing such as the top-k and reverse top-k for uncertain big data has become important. In this paper, we model firstly probabilistic reverse top-k queries over uncertain big data for the discrete situation, in both monochromatic and bichromatic cases, denoted by MPRT and BPRT queries, respectively. We determine the partitions of solution space of MPRT queries and provide in theory a mathematical model for solving arbitrary dimensional data space. Additionally, we propose effective pruning heuristics to reduce the search space of BPRT queries. Moreover, efficient query procedures are presented seamlessly with integration of the proposed pruning strategies. Extensive experiments demonstrate the efficiency and effectiveness of our proposed approaches with various experimental settings.

Risk Analysis Technique on Inconsistent Interview Big Data Based on Rough Set Approach

Rough set theory is relativly new to area of soft computing to handle the uncertain big data efficiently. It also provides a powerful way to calculate the importance degree of vague and uncertain big data to help in decision making. Risk assessment is very important for safe and reliable investment. Risk management involves assessing the risk sources and designing strategies and procedures to mitigate those risks to an acceptable level. In this paper, we emphasize on classification of different types of risk factors and find a simple and effective way to calculate the risk exposure.. The study uses rough set method to classify and judge the safety attributes related to investment policy. The method which based on intelligent knowledge accusation provides an innovative way for risk analysis. From this approach, we are able to calculate the significance of each factor and relative risk exposure based on the original data without assigning the weight subjectively.

A Data Analytic Algorithm for Managing, Querying, and Processing Uncertain Big Data in Cloud Environments

Big data are everywhere as high volumes of varieties of valuable precise and uncertain data can be easily collected or generated at high velocity in various real-life applications. Embedded in these big data are rich sets of useful information and knowledge. To mine these big data and to discover useful information and knowledge, we present a data analytic algorithm in this article. Our algorithm manages, queries, and processes uncertain big data in cloud environments. More specifically, it manages transactions of uncertain big data, allows users to query these big data by specifying constraints expressing their interests, and processes the user-specified constraints to discover useful information and knowledge from the uncertain big data. As each item in every transaction in these uncertain big data is associated with an existential probability value expressing the likelihood of that item to be present in a particular transaction, computation could be intensive. Our algorithm uses the MapReduce model on a cloud environment for effective data analytics on these uncertain big data. Experimental results show the effectiveness of our data analytic algorithm for managing, querying, and processing uncertain big data in cloud environments.

Dynamic Guaranteed Cost Compression for Time Series Big Data

Query processing technology has recently received a lot of attention in the business intelligence and information service communities. However, the existing approaches can not efficiently optimize the query performance in the uncertain big data environment. In this paper, we propose QPPUBG, a novel and efficient query processing platform for uncertain big data. QPPUBG mainly includes four modules: (i) query equivalence reconstructing for uncertain big data; (ii) multiple query optimization over probability relation components; (iii) query execution plan constructing over probability relation components, and (iv) physical implementation solution of query for uncertain big data. Specially, QPPUBG can support the possible world instance semantics and efficiently handle arbitrary decision spaces. Moreover, QPPUBG can seamlessly integrate the above four modules into the modern parallel computation frameworks. We present the extensive experiments that demonstrate QPPUBG is both efficient and effective.

Uncertain Unstructured Data Management: Put All Apple in Nine Basket

In recent era, “Big Data” has become a new generic term. Big Data is revamping science, healthcare, finance, engineering, medicine, business and ultimately society itself. Big data has already takes place in the industry and, is altering the uncertain unstructured data management dramatically within the industry and its core business processes. It’s going to significantly impact on business and data management practices within the company [1]. The continued declining costs of storage and computing power will make big data attractive [3][4]. Dealing with big data requires your data management practices to understand and embrace the new reality. We hear the term “unstructured data” often. It’s evolved up as the enormous challenge of big data and often cited as the reason why traditional relational databases don’t meet the needs of Big Data. But the above specified point doesn’t describe the challenges faced by organization with unstructured data. While most research paper is only Big data management into Three V s. but I believe there are “Nine V”s for uncertain unstructured Big Data Management because big data is about joining the dots, plotting the relationship, seeing the sensitivities, understating the cause and effect, in this paper we are classifying the uncertain Unstructured Big Data Management into nine V’s and it’s details are:

Recent advances in autonomic provisioning of big data applications on clouds

Cloud computing assembles large networks of virtualised ICT services such as hardware resources (such as CPU, storage, and network), software resources (such as databases, application servers, and web servers) and applications. Big Data applications have become a common phenomenon in domain of science, engineering, and commerce. Large-scale, heterogeneous, and uncertain Big Data applications are becoming increasingly common, yet current cloud resource provisioning methods do not scale well and nor do they perform well under highly unpredictable conditions (data volume, data variety, data arrival rate, etc.). Much research effort have been paid in the fundamental understanding, technologies, and concepts related to autonomic provisioning of cloud resources for Big Data applications, to make cloud-hosted Big Data applications operate more efficiently, with reduced financial and environmental costs, reduced under-utilisation of resources, and better performance at times of unpredictable workload. Targeting the aforementioned research challenges, this special issue compiles recent advances in Autonomic Provisioning of Big Data Applications on Clouds. The special issue articles are briefly summarized.

Trustworthy answers for top-k queries on uncertain Big Data in decision making

Effectively extracting reliable and trustworthy information from Big Data has become crucial for large business enterprises. Obtaining useful knowledge to enable better decisions to be made in order to improve business performance is not a trivial task. The most fundamental challenge for Big Data extraction is to handle the uncertainty of data to meet emerging business needs, such as marketing analysis, future prediction and decision making. In this paper, we firstly propose a novel approach called Dominating Top-k Aggregate Query (DA-Topk) to provide trustworthy and reliable informative knowledge from uncertain Big Data by combining the techniques of skyline and top-k queries. Then, we design a number of pruning rules to reduce the search space and terminate the ranking process as early as possible. Next, we provide a deeper analysis regarding the satisfaction of the six ranking properties (i.e. exact-k, containment, unique-rank, value-invariance, stability and faithfulness) between our approach and existing approaches to demonstrate that our method is the only one which satisfied all of these properties. Extensive experiments with both real and synthetic data sets have been conducted to verify the efficiency and effectiveness of our proposed approach compared to the state-of-the-art approaches. Our approach can help managers make strategic decisions quickly and accurately in competitive market places.