APRA: An approximate parallel recommendation algorithm for Big Data

Hammou, Badr Ait; Lahcen, Ayoub Ait; Mouline, Salma

doi:10.1016/j.knosys.2018.05.006

Cited by 31 publications

(10 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Base on the traditional recommendation techniques, some modified algorithms have emerged such as CBF-Separated, CF-CBF Separated and CBF-CF Parallel algorithms [87]. The CBF-Separated algorithm is built upon the pure CBF algorithm.…”

Section: Hybrid Methods (Hm)mentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

View full text Add to dashboard Cite

Globally, the recommendation services have become important due to the fact that they support e-commerce applications and different research communities. Recommender systems have a large number of applications in many fields, including economic, education, and scientific research. Different empirical studies have shown that the recommender systems are more effective and reliable than the keyword-based search engines for extracting useful knowledge from massive amounts of data. The problem of recommending similar scientific articles in scientific community is called scientific paper recommendation. Scientific paper recommendation aims to recommend new articles or classical articles that match researchers' interests. It has become an attractive area of study since the number of scholarly papers increases exponentially. In this paper, we first introduce the importance and advantages of the paper recommender systems. Second, we review the recommendation algorithms and methods, such as Content-based, collaborative filtering, graph-based, and hybrid methods. Then, we introduce the evaluation methods of different recommender systems. Finally, we summarize the open issues in the paper recommender systems, including cold start, sparsity, scalability, privacy, serendipity, and unified scholarly data standards. The purpose of this survey is to provide comprehensive reviews on the scholarly paper recommendation.

show abstract

Section: Hybrid Methods (Hm)mentioning

confidence: 99%

Scientific Paper Recommendation: A Survey

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Such big data, whether spatial or non-spatial, requires significant attention to support queries efficiently. These queries are used in various applications and services such as social recommendations (Zhang & Chow 2015;Hammou et al 2018), social community and event detection (Al Aghbari et al 2019;Sapountzi & Psannis 2018), wireless sensor networks (Al Aghbari et al 2012Aghbari et al , 2013, image analysis (Dinges et al 2011;Kubo et al 2003), smart cities (Hanif et al 2018;Alsaafin et al 2018), and urban route planning Babar et al (2019). As a result, spatial data mining has emerged, which deals with gaining knowledge, forming spatial relations, and mining interesting trends and patterns from geo-tagged data (Al Jawarneh et al 2018;Garaeva et al 2017).…”

Section: Introductionmentioning

confidence: 99%

SparkNN: A Distributed In-Memory Data Partitioning for KNN Queries on Big Spatial Data

Aghbari

Ismail

Kamel

2020

Data Science Journal

View full text Add to dashboard Cite

The increase in GPS-enabled devices and proliferation of location-based applications have resulted in an abundance of geotagged (spatial) data. As a consequence, numerous applications have emerged that utilize the spatial data to provide different types of location-based services. However, the huge amount of available spatial data presents a challenge to the efficiency of these location-based services. Although the advent of big data frameworks like Apache Spark has enabled the processing of large amounts of data efficiently, they are designed for general (non-spatial) data. That is due to the build-in data partitioning mechanism that does not take into account the spatial proximity of the data. Therefore, these big data frameworks cannot be readily used for spatial analytics such as efficiently answering spatial queries. To fill this gap, this paper proposes SparkNN, an in-memory partitioning and indexing system for answering spatial queries, such as K-nearest neighbor, on big spatial data. SparkNN is implemented on top of Apache Spark and consists of three layers to facilitate efficient spatial queries. The first layer is a spatial-aware partitioning layer, which partitions the spatial data into several partitions ensuring that the load of the partitions is balanced and data objects with close proximity are placed in the same, or neighboring, partitions. The second layer is a local indexing layer, which provides a spatial index inside each partition to speed up the data search within the partition. The third layer is a global index, which is placed in the master node of Spark to route spatial queries to the relevant partitions. The efficiency of SparkNN was evaluated by extensive experiments with big spatial datasets. The results show SparkNN significantly outperforms the state-of-the-art Spark system when evaluated on the same set of queries.

show abstract

“…Given a large-scale network, the time complexity of the graph algorithms increases exponentially compared to the number of vertices [1]. Thus, to speed up the performance of graph algorithms, it's recommended to use distributed system to speed up analytical tasks [16]. This technique is widely used in NoSQL databases.…”

Section: Introductionmentioning

confidence: 99%

DHPV: A Distributed Algorithm for Large-Scale Graph Partitioning

Adoni

Nahhal

Krichen

et al. 2020

Preprint

View full text Add to dashboard Cite

Big graphs are part of the movement of "Not Only SQL" databases (also called NoSQL) focusing on the relationships between data, rather than the values themselves. The data is stored in vertices while the edges model the interactions or relationships between these data. They offer flexibility in handling data that is strongly connected to each other. The analysis of a big graph generally involves exploring all of its vertices. Thus, this operation is costly in time and resources because big graphs are generally composed of millions of vertices connected through billions of edges. Consequently, the graph algorithms are expansive compared to the size of the big graph, and are therefore ineffective for data exploration. Thus, partitioning the graph stands out as an efficient and less expensive alternative for exploring a big graph. This technique consists in partitioning the graph into a set of k sub-graphs in order to reduce the complexity of the queries. Nevertheless, it presents many challenges because it is an NP-complete problem. In this article, we present DPHV (Distributed Placement of Hub-Vertices) an efficient parallel and distributed heuristic for large-scale graph partitioning. An application on a real-world graphs demonstrates the feasibility and reliability of our method. The experiments carried on a 10-nodes Spark cluster proved that the proposed methodology achieves significant gain in term of time and outperforms JA-BE-JA, Greedy, DFEP.

show abstract

APRA: An approximate parallel recommendation algorithm for Big Data

Cited by 31 publications

References 34 publications

Scientific Paper Recommendation: A Survey

Scientific Paper Recommendation: A Survey

SparkNN: A Distributed In-Memory Data Partitioning for KNN Queries on Big Spatial Data

DHPV: A Distributed Algorithm for Large-Scale Graph Partitioning

Contact Info

Product

Resources

About