As the volume of data grows rapidly, storing big data in a single data center is no longer feasible. Hence, companies have developed two scenarios to store their big data in multiple data centers. In the first scenario, the company’s big data are distributed in multiple data centers without data replication. In the second scenario, data are also stored in multiple data centers but important data are replicated in these data centers to increase data safety and availability. However, in these scenarios, analyzing big data distributed in multiple data centers becomes a challenging task. In this paper, we propose two data distribution strategies to support big data analysis across geo-distributed data centers. In these strategies, we use the recent Random Sample Partition data model to convert big data into sets of random sample data blocks and distribute these data blocks into multiple data centers either without replication or with replication. In analyzing big data in multiple data centers without replication, we randomly select samples of data blocks from multiple data centers and download the sample data blocks to one data center for analysis. In the second strategy with replication of data blocks, we can analyze big data on any data center by randomly selecting a sample of data blocks replicated from other data centers. This strategy avoids data transformation between data centers. We demonstrate the performance of the two strategies in big data analysis by using simulation results produced on one local data center and four AWS data centers in North America, Asia, and Australia.

Event-based social networks (EBSNs) have gained increasing popularity and rapid growth, EBSNs provide services for users to create events and make plan to attend. Developing and creating recommendation models are important and hot issues in EBSNs in recent years, such as event recommendation to users. Although several recommendation models have been proposed, event attendees recommendation models are not fully studied. In this paper, we study the event attendees recommendation problem through empirical experiments. Because of the nature of new events and severe data sparsity in EBSNs, traditional recommender systems work less efficiently for event attendees recommendation problem. To solve this problem, we propose a new location-based topic model that is based on scores of users computed from three major factors extracted from previously attended events, namely content, location and time. The proposed model includes two phases. The first phase uses the topic modeling Latent Dirichlet Allocation (LDA) and Jensen Shannon divergence to compute the similarity of events based on their contents. The spatial and temporal factors are also calculated. The scores of previous events with an upcoming event are computed from a combination of these three factors. Previous events with high scores are selected, then users who are extracted from the selected events are scored by temporal factors of these events in the second phase. Finally, we recommend users with top scores to the upcoming event. A series of experiments were conducted on real data collected from Meetup Event and the results have demonstrated the improvement of our model over baseline methods.