In many analytic settings join operations are fundamental as data is dispersed across different data sets (SQL or NoSQL tables, .csv files recording logs, click streams, KPIs from system/network monitoring, IoT telemetry, etc). However, in the era of big data the join operation can become exorbitantly expensive in terms of execution times and/or memory/space footprints.

Modern databases face formidable challenges when called to join (several) massive tables. Joins (especially when entailing many-to-many joins) are very time- and resource-consuming, join results can be too big to keep in memory, and performing analytics/learning tasks over them costs dearly in terms of time, resources, and money (in the cloud). Moreover, although random sampling is a promising idea to mitigate the above problems, the current state of the art leaves lots of room for improvements. With this paper we contribute a principled solution, coined PGMJoins. PGMJoins adapts Probabilistic Graphical Models to deriving provably random samples of the join result for (n-way) key joins, many-to-many joins, and cyclic and acyclic joins. PGMJoins contributes optimizations both for deriving the structure of the graph and for PGM inference. It also contributes a novel Sum-Product Message Passing Algorithm (SP-MPA) to make a uniform sample of the joint distribution (join result) efficiently and a novel way to deal with cyclic joins. Despite the use of PGMs, the learned joint distribution is not approximated, and the uniform samples are drawn from the true distribution. Our experimentation using queries and datasets from TPC-H, JOB, TPC-DS, and Twitter shows PGMJoins to outperform the state of the art (by 2X-28X).

Numerous evolutionary algorithms have been proposed which are inspired by the amazing lives of creatures, such as animals, insects, and birds. Each inspired algorithm has its own advantages and disadvantages, and has its own way to accomplish exploration and exploitation. In this paper, a new evolutionary algorithm with novel concepts, called Wildebeests Herd Optimization (WHO), is proposed. This algorithm is inspired by the splendid life of wildebeests in Africa. Moving and migration are inseparable from wildebeests’ lives. When a wildebeest wants to choose its path during migration, it considers the best path known to itself, the location of the more mature wildebeests in the crowd, and the direction of wildebeests with high mobility. The WHO algorithm imitates these traits, and can concurrently explore and exploit the search space. For validating WHO, it is applied to optimization problems and data mining tasks. It is demonstrated that WHO outperforms other evolutionary algorithms, such as genetic algorithm (GA) and particle swarm optimization, in the assessed problems. Then, WHO is applied to the customer segmentation problem. Customer segmentation is one of the most important tasks of data mining, especially in the banking sector. In this paper, the customers of a bank with current accounts are segmented using WHO based on four aspects: profitability, cost, loyalty and credit; some of these aspects are calculated in a novel way. The results were welcome by the bank authorities.