With data storage and sharing services in the cloud, users can easily modify and share data as a group. To ensure shared data integrity can be verified publicly, users in the group need to compute signatures on all the blocks in shared data. Different blocks in shared data are generally signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks which were previously signed by this revoked user must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing the idea of proxy re-signatures, we allow the cloud to resign blocks on behalf of existing users during user revocation so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Moreover, our mechanism is able to support batch auditing by verifying multiple auditing tasks simultaneously. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.

Public Auditing For Shared Data with Efficient User Revocation in the Cloud

The purpose of this paper is to propose a combined data mining approach for analyzing and profiling customers in video on demand (VoD) services. The proposed approach integrates clustering and association rule mining. For customer segmentation, the LRFMP model is employed alongside the k-means and Apriori algorithms to generate association rules between the identified customer groups and content genres. The applicability of the proposed approach is demonstrated on real-world data obtained from an Internet protocol television (IPTV) operator. In this way, four main customer groups are identified: “high consuming-valuable subscribers”,” less consuming subscribers”,” less consuming-loyal subscribers” and “disloyal subscribers”. In detail, for each group of customers, a different marketing strategy or action is proposed, mainly campaigns, special-day promotions, discounted materials, offering favorite content, etc. Further, genres preferred by these customer segments are extracted using the Apriori algorithm. The results obtained from this case study also show that the proposed approach provides an efficient tool to form different customer segments with specific content rental characteristics, and to generate useful association rules for these distinct groups. The proposed combined approach in this research would be beneficial for IPTV service providers to implement effective CRM and customer-based marketing strategies.

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A Combined Approach for Customer Profiling in Video on Demand Services Using Clustering and Association Rule Mining

Every day quintillion bytes of data is generated; 90% of which has been created in the last two years alone, from this it can predict the amount of data that will be generated in future. It is necessary, to introduce some techniques for analyzing such a huge amount data to face the challenges and to deal with limitation of ‘Big data Analysis’. Such Big Data analysis can be used nearly in every aspect of our modern society, including mobile services, retail and consumer services, manufacturing, business intelligence, financial services and robotics. The motive of this paper is to understand how big data is generated and the necessity of analyzing such data. This paper also gives a short glimpse of Big Data analysis implications in the real world and its role in every field along with challenges and advantages. This paper also explores various techniques, algorithms, systems of big data analytics in various sectors of digital world.

Survey on Big data Analytics for digital world

Big data is a modern age concept that is used to process large amount of data in various fields ranging from medical, remote Sensing, etc, The Medical Sphere is a tangential aspect to every individual's life A break-through can be achieved by Prognotive computing Prognotive Computing is related to big data analytics as the process may require the collection, processing and analysis of extremely large volume of biomedical data originating from a wide range of experiments and surveys collected by hospitals, laboratories, Pharmaceutical companies or even social media which is implemented by using tools for Big Data The prognosis result will improve the efficiency in providing better living to people

Health Monitoring System by Prognotive Computing Using Big Data Analytics

In data mining, various techniques are applied on large sets of data. These large sets of data are known as big data. For extraction and retrieval of desired information or pattern from big amount of data and quantity of knowledge, we tend to use huge data processing. To maintain working of “big data,” various logics are used. Today, because of many online applications, product price is highly dynamic and current price can be compared by the customers by using different applications. In our work, we have used modified K-means clustering method and divided online data. Dataset is divided into 10 clusters for analysis. In the last section, the conclusion based on I/O time and computational time is given.

Predicting Dynamic Product Price by Online Analysis: Modified K-Means Cluster

The enhancement of cloud computing make storage outsourcing becomes an exceeding trend, which result a secure data auditing a cool topic that emerge in research literature. Recently some researches consider the problem of efficient and secure public data authentication inspection for shared dynamic data. However, these schemes are still not secure against the collusion and leakage of cloud storage server from unauthorized attacker and revoked group users during user revocation in cloud storage system. In this paper, there will be auditing the integrity of shared data with dynamic groups in cloud. A new user can be added into the group and an existing group member can be revoked by preserving privacy including data backup based on vector commitment and verifier-local revocation group signature. This scheme supports the public validation and efficient user revocation and also some nice properties such as traceability, efficiency, confidently, countability. Finally, the security and experimental analysis show that our scheme is also secure and efficient.

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Group User Revocation and Integrity Auditing of Shared Data in Cloud Environment

IPTV Software Process and Workflow

Spatial data is a data having a geological information. This paper explores the use of Hadoop framework to improve the performance of Apriori algorithm for spatial data mining. FP growth algorithm is better than Apriori but it fails in certain situations. By applying the Apriori algorithm parallely using Hadoop framework to spatial data, we can perform well as compare to FP growth. This paper includes clustering based on geological location, classification based on mineral resource type and spatial coherence between mineral resources. Spatial data mining find out the different association rules by observing the spatial data by using Apriori algorithm. The result of the paper will indicate the accurate prediction of occurrence of commodity with respect to other commodity of mineral resources.

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Improving performance of apriori algorithm using hadoop

The Voice User Interface (VUI) for human-computer interaction has received wide acceptance, due to which the systems for speech recognition in regional languages are now being developed, taking into account all of the dialects. Because of the limited availability of the speech corpus (SC) of regional languages for doing research, designing a speech recognition system is challenging. This contribution provides a Parallel Big Bang-Big Crunch (PB3C)-based mechanism to automatically evolve the optimal architecture of LSTM (Long Short-Term Memory). To decide the optimal architecture, we evolved a number of neurons and hidden layers of LSTM model. We validated the proposed approach on Marathi speech recognition system. In this research work, the performance comparisons of the proposed method are done with BBBC based LSTM and manually configured LSTM. The results indicate that the proposed approach is better than two other approaches.

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Ravindra P. Bachate

Papers

Survey on Big data Analytics for digital world

Group User Revocation and Integrity Auditing of Shared Data in Cloud Environment

IPTV Software Process and Workflow

Improving performance of apriori algorithm using hadoop

Parallel Big Bang-Big Crunch-LSTM Approach for Developing a Marathi Speech Recognition System