Sample Hadoop Projects
Category : Sample Hadoop Projects
Sample Hadoop Projects
Get Sample Hadoop Projects Abstract From Hadoop Solutions.
- A cache-based method for SPARQL query processing Nowadays, the use of semantic web technologies is growing fast. On the other hand, the downside of these systems, is their poor performance compared to traditional database management systems. To tackle this problem, optimizations such as caching methods on conventional relational databases can be adapted on semantic web technologies. In this paper, we stored and reused the results of SPARQL queries in an RDF triple store which is implemented on Hadoop file system. Moreover, a method for naming the files containing cached results and also a heuristic algorithm for cache replacement and file deletion are proposed.
- A Data Processing Algorithm in EPC Internet of Things With the rapid growth of data which sensed by the sensor nodes deployed in the logistics center, the current data processing methods have been impossible to meet the requirements of rapid and efficient analysis and processing for big data. In order to satisfy the demand, this paper designs a data processing algorithm which is implemented by Hadoop. The experimental results show that our method can greatly improve the system efficiency of data processing.
- Processing performance on Apache Pig, Apache Hive and MySQL cluster MySQL Cluster is a famous clustered database that is used to store and manipulate data. The problem with MySQL Cluster is that as the data grows larger, the time required to process the data increases and additional resources may be needed. With Hadoop and Hive and Pig,processing time can be faster than MySQL Cluster. In this paper, three data testers with the same data model will run simple queries and to find out at how many rows Hive or Pig is faster than MySQL Cluster. The data model taken from GroupLens Research Project  showed a result thatHive is the most appropriate for this data model in a low-cost hardware environment.
- Method and system for educational networking1 In this paper we unveil a major shift in education paradigm, a system,method, and an IT infrastructure for educational networking, configured to provide networking and services to users on client devices coupled to the computer server over the communications network, including automating application submission and processing at educational centers; searching, sorting, and customizing the schools as well as exploring funding opportunities and laboratories; the clients will also be able to create school application portfolios for shortlisted schools. Modern processing tools such as Big Data/Hadoop along with evolutionary algorithms, pave the way towards introducing the most suitable services to the clients based on their background, needs, and behavior. We present a class of social networks that unifies and modernizes academic and educationalrelated services.
- Efficient traffic speed forecasting based on massive heterogenous historical data Drivers dream of foreseeing traffic condition to enjoy efficient driving experience at all times. Given the historical patterns for different locations and different time, people should be able to guess the possible trafficspeed in a near future moment. What is difficult and interesting for this task is that we need to filter the useful data that could help us for the next moment traffic speed prediction from a massive amount of historicaldata. On the other hand, the traffic condition could be highly dynamic and we can only give a reliable traffic prediction by using the most updated model for prediction. This implies that frequent retraining is necessary. To conquer the task, we propose a lazy learning approach fortraffic speed prediction given massive historical data. The approach integrates the kNN and Gaussian process regression for efficient and robust traffic speed prediction. kNN can help us to select the most informative data for Gaussian process Regression using a big dataframework. Thanks for the most recent progress of big data research, the processing of massive data for prediction in close to real time has become possible now compared to any time in the past. We aim at using a Hadoop framework for the prediction given heterogeneous dataincluding traffic data such as speed, flow, occupancy, and weather data.
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