This is in response to recent feedback from some readers, which requires some clarifications regarding our IEJ oin algorithm published in [1]. The feedback revolves around four points: (1) a typo in our illustrating example of the join process; (2) a naming error for the index used by our algorithm to improve the bit array scan; (3) the sort order used in our algorithms; and (4) a missing explanation on how duplicates are handled by our self join algorithm.

Inequality joins, which join relational tables on inequality conditions, are used in various applications. While there have been a wide range of optimization methods for joins in database systems, from algorithms such as sort-merge join and band join, to various indices such as B + -tree, R * -tree and Bitmap, inequality joins have received little attention and queries containing such joins are usually very slow. In this paper, we introduce fast inequality join algorithms. We put columns to be joined in sorted arrays and we use permutation arrays to encode positions of tuples in one sorted array w.r.t. the other sorted array. In contrast to sort-merge join, we use space efficient bit-arrays that enable optimizations, such as Bloom filter indices, for fast computation of the join results. We have implemented a centralized version of these algorithms on top of PostgreSQL, and a distributed version on top of Spark SQL. We have compared against well known optimization techniques for inequality joins and show that our solution is more scalable and several orders of magnitude faster.

Classification of short text messages is becoming more and more relevant in these years, where billion of users use online social networks to communicate with other people. Understanding message content can have a huge impact on many data analysis processes, ranging from the study of online social behavior to targeted advertisement, to security and privacy purposes. In this paper, we propose a new unsupervised knowledge-based classifier for short text messages, where each category is represented by an ego-network. A short text is classified into a category depending on how far its words are from the ego of that category. We show how this technique can be used both in single label and in multi-label classification, and how it outperforms the state of the art for short text messages classification.