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This document is a retraction note for an article titled "FSS-SDD: fuzzy-based semantic search for secure data discovery from outsourced cloud data" published in the journal Soft Computing. The publisher has retracted the article due to concerns about compromised editorial handling and peer review process, inappropriate references, and the article not being in scope of the journal or the guest-edited issue. The author S Karthik disagrees with the retraction, while authors M. Ananthi, R. Sabitha, and J. Shanthini have not responded to correspondence regarding the retraction. The online version of the original article can still be found on the publisher's website. [Extracted from the article]

Stock market data is a time-series data in which stock value varies depends on time. Prediction of the stock market is an endeavor to assess the future value of a company's stock rate which will increase the investor's profit. The accurate prediction of stock market analysis is still a challenging task. The proposed system predicts stock price of any company mentioned by the user for the next few days. Using the predicted stock price and datasets collected from various sources regarding a certain equity, the overall sentiment of the stock is predicted. The prediction of stock price is done by regression and candlestick pattern detection. The proposed system generates signals on the candlestick graph which allows to predict market movement to a sufficient level of accuracy so that the user is able to judge whether a stock is a 'Buy/Sell' and whether to short the stock or go long by delivery. The prediction accuracy of the stock exchange has analyzed and improved to 85% using machine learning algorithms. [ABSTRACT FROM AUTHOR]

In current decade, cloud computing has become more prevalent and utilized for managing more sensitive data that are stored over cloud storage. While outsourcing the sensitive and private data on to the cloud, security is the major factor to be concentrated in efficient data search using keywords. As is auspicious, security of selective keyword search can be achieved using the powerful cryptographic technique called encryption. That is, the data that are to be outsourced have to be encrypted before storing it on cloud. Symmetric encryption is used between the data owner and the customer, whereas asymmetric encryption is used between the data owner and the cloud server. In such cases, the data discovery and retrieval have become more stimulating process. However, there are many methodologies developed for searching the encrypted outsourced data from the cloud. In this paper, the efficient data discovery process is focused and enhanced with the developed model called fuzzy-based semantic search for secure data discovery (FSS-SDD). A multi-valued logic called fuzzy logic is used, which has truth values of variables ranging between 0 and 1. It is used where the truth values range between completely true and completely false. The fuzzy logic-based semantic search improves the searching experience of the end user, by finding and retrieving the exact matching files for corresponding search files given by the user. Moreover, the model discovers the closely relevant matches using the semantic similarities, in cases, when the exact matches are not avail. For reducing the false positive rates, grading mechanism is enforced. Using the proposed FSS-SDD model, the processing overhead on new updates is effectively reduced and security in data retrieval is guaranteed. [ABSTRACT FROM AUTHOR]

The Modern era has highly dynamic, heterogeneous and massive data volumes, generated from sensor networks, social media and telecommunications, stock market analyses and the Internet, etc. makes constant query processing quite challenging in processing real-time data, which exist as streams and undergo dynamic changes. Large volumes of data can be efficiently handled by partitioning them into clusters followed by Indexing. An efficient clustering and indexing method is required to process continuous queries for retrieving data streams. A new index structure called adaptive clustering and block-based indexing (ACBBI) is proposed, which is a fusion of cluster-based and block-based techniques to process continuous queries. The incoming data are clustered and stored as blocks using the adaptive clustering method and further indexed by the adaptive indexing approach. Livestock market values that are time variant are used for experimentation. The experimental analysis demonstrates that the ACBBI tree structure significantly decreases half of the space cost, scales better with increasing data size and improves the retrieval rate 30% more than an existing CKDB approach. [ABSTRACT FROM AUTHOR]