Hadoop Final year Projects
BCA final year Projects in Tirupur
BCA final year projects in Tirupur
Why Glim Technologies, Best BCA Final year projects institute in Tiruppur?
Best BCA Final year projects in Tirupur provided by best BCA Final year projects in Tirupur. Best BCA Final year projects provided by certified professional with more than 10 years of experience. BCA Projects is given by our real time experts who have more experience in that Particular domain. The method will be based on the real time scenarios with 100% of IEEE projects. BCA Projects content is framed to cover both experienced and Non-experienced students in Computers.
What we do at Glim Technologies, best BCA Final year projects in Tirupur?
Glim Technologies, provides latest IEEE Projects for BCA students. Our Project faculty has lot of Real time experience and well qualified professional to deliver quality projects. The faculty has 10+ years of experience and handled many corporate projects in India. Glim Technologies is recognized as Best BCA Final year projects in Tirupur by the students. For all the students, Domain basics is covered to understand the fundamental concepts.
Whom we do projects at GLIM, best BCA Final year projects in Tirupur?
We are more experienced and providing IEEE projects and real time projects across all experience 0 -18 years and we have separate Projects content for BCA students. We customize the syllabus covered according to the role requirements in the IEEE.
BCA final year projects in Tirupur
- Achieving Efficient and Privacy-Preserving Cross-Domain Big Data De-duplication in Cloud
- Secure information de-duplication will considerably cut back the communication and storage overheads in cloud storage services, and has potential applications in our massive data-driven society. Existing information de-duplication schemes area unit usually designed to either resist brute-force attacks or make sure the potency and information availableness, however not each conditions. We tend to are not attentive to any existing theme that achieves responsibility, within the sense of reducing duplicate data revelation (e.g., to see whether or not plaintexts of 2 encrypted messages area unit identical). During this paper, we tend to investigate a three-tier cross-domain design, Associate in Nursing propose an economical and privacy-preserving massive information de-duplication in cloud storage (hereafter spoken as EPCDD). EPCDD achieves each privacy-preserving and information availableness, and resists brute-force attacks. Additionally, we tend to take responsibility into thought to supply higher privacy assurances than existing schemes. We tend to then demonstrate that EPCDD outperforms existing competitive schemes, in terms of computation, communication and storage overheads. Additionally, the time complexness of duplicate search in EPCDD is index.
- AHDFS: An Erasure-Coded Data Archival System for Hadoop Clusters
We propose associate degree erasure-coded knowledge depository system referred to as aHDFS for Hadoop clusters, wherever RS(k+r;k) codes square measure utilized to archive knowledge replicas within the Hadoop distributed filing system or HDFS. We have a tendency to develop 2 depository methods (i.e., aHDFS-Grouping and aHDFS-Pipeline) in aHDFS to hurry up the information depository method. aHDFS-Grouping – a Map-Reduce-based knowledge archiving theme -keeps every mapper’s intermediate output Key-Value pairs in a verynative key-value store. With the native store in situ, aHDFS-Grouping merges all the intermediate key-value trys with a similar key into one single key-value pair, followed by shuffling the only Key-Value try to reducersto generate final parity blocks. AHDFS-Pipeline forms a knowledge |a knowledge|an information} depository pipeline victimization multiple data node in a very Hadoop cluster. AHDFS-Pipeline delivers the incorporate single key-value try to a resulting node’s native key-value store. Last node within the pipeline is liable for out golf stroke parity blocks. We have a tendency to implement aHDFS in a very real-world Hadoop cluster. The experimental results show that aHDFS-Grouping and Ahdfs-Pipeline speed up Baseline’s shuffle and scale back phases by an element of ten and five, severally. Once block size is larger than 32MB, aHDFS improves the performance of HDFS-RAID and HDFS-EC by about thirty one.8% and 15.7%, severally.
- Privacy-Preserving Multi-keyword Top-k Similarity Search Over Encrypted Data (Cloud Computing)
Cloud computing provides individuals and enterprises massive computing power and scalable storage capacities to support a variety of big data applications in domains like health care and scientific research, therefore more and more data owners are involved to outsource their data on cloud servers for great convenience in data management and mining. However, data sets like health records in electronic documents usually contain sensitive information, which brings about privacy concerns if the documents are released or shared to partially un-trusted third-parties in cloud. A practical and widely used technique for data privacy preservation is to encrypt data before outsourcing to the cloud servers, which however reduces the data utility and makes many traditional data analytic operators like keyword-based top-k document retrieval obsolete. In this paper, we investigate the multi-keyword top-k search problem for big data encryption against privacy breaches, and attempt to identify an efficient and secure solution to this problem. Specifically, for the privacy concern of query data, we construct a special tree-based index structure and design a random traversal algorithm, which makes even the same query to produce different visiting paths on the index, and can also maintain the accuracy of queries unchanged under stronger privacy. For improving the query efficiency, we propose a group multi-keyword top-k search scheme based on the idea of partition, where a group of tree-based indexes are constructed for all documents. Finally, we combine these methods together into an efficient and secure approach to address our proposed top-k similarity search. Extensive experimental results on real-life data sets demonstrate that our proposed approach can significantly improve the capability of defending the privacy breaches, the scalability and the time efficiency of query processing over the state-of-the-art methods
- Query Expansion with Enriched User Profiles for Personalized Search Utilizing Folksonomy Data (Data Mining)
Query expansion has been widely adopted in Web search as a way of tackling the ambiguity of queries. Personalized search utilizing folksonomy data has demonstrated an extreme vocabulary mismatch problem that requires even more effective query expansion methods. Co-occurrence statistics, tag-tag relationships and semantic matching approaches are among those favored by previous research. However, user profiles which only contain a user’s past annotation information may not be enough to support the selection of expansion terms, especially for users with limited previous activity with the system. We propose a novel model to construct enriched user profiles with the help of an external corpus for personalized query expansion. Our model integrates the current state-of-the-art text representation learning framework, known as word embeddings, with topic models in two groups of pseudo-aligned documents. Based on user profiles, we build two novel query expansion techniques. These two techniques are based on topical weights-enhanced word embeddings, and the topical relevance between the query and the terms inside a user profile respectively. The results of an in-depth experimental evaluation, performed on two real-world datasets using different external corpora, show that our approach out performs traditional techniques, including existing non-personalized and personalized query expansion methods.
- Frequent Itemsets Mining With Differential Privacy Over Large-Scale Data (Data Mining)
Frequent itemsets mining with differential privacy refers to the matter of mining all frequent itemsets whose supports ar higher than a given threshold in an exceedingly given transactional dataset, with the constraint that the strip-mined results mustn’t break the privacy of any single dealing. Current solutions for this drawback cannot well balance potency, privacy, and information utility over large-scale information. Toward this finish, we have a tendency to propose associate economical, differential personal frequent itemsets mining formula over large-scale information. supported the concepts of sampling and dealing truncation victimization length constraints, our formula reduces the computation intensity, reduces mining sensitivity, and therefore improves information utility given a set privacy budget. Experimental results show that our formula achieves higher performance than previous approaches on multiple datasets.
- Privacy-Preserving Social Media Data Publishing for Personalized Ranking-Based Recommendation (Machine learning)
Personalized recommendation is crucial to assist users notice pertinent info. It usually depends on an oversizedassortment of user knowledge, above all users’ on-line activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, cathartic such user activity knowledge makes users liable to logical thinkingattacks, as non-public knowledge (e.g., gender) will usually be inferred from the users’ activity knowledge. during this paper, we have a tendency to planned PrivRank, a customizable and continuous privacy-preserving social media knowledge business framework protective users against logical thinking attacks whereas sanctionativecustomized ranking-based recommendations. Its key plan is to endlessly change user activity knowledge such the privacy outflow of user-specified non-public knowledge is decreased beneath a given knowledge distortion budget, that bounds the ranking loss incurred from the info obfuscation method so as to preserve the utility of the info for sanctionative recommendations. Associate in Nursing empirical analysis on each artificial and real-world knowledgesets shows that our framework will expeditiously give effective and continuous protection of user-specified non-public data, whereas still protective the utility of the obfuscated knowledge for customized ranking-based recommendation. Compared to progressive approaches, PrivRank achieves each an improved privacy protection and the next utility altogether the ranking-based recommendation use cases we have a tendency totested.
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“This is Suresh. Technically they handle the projects in a great way. I did my BCA Final year projects in Tirupur. That was a good experience to learn lot new things in technologies.”
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“Thank you so much to Glim technologies for helping me to complete my BCA final year project in tirupur within short time period. Its really great help. They done project very well. So, they take this on their head to complete it with limited time duration.”
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