Self-service data preparation enables end users to prepare data by themselves. However, the plethora of possible data preparation steps can overwhelm the user. We introduce a score-based preparator ranking approach to propose preparator candidates in a context-specific manner. To this end, we give scoring functions for a selected set of preparators and outline future work towards a full-fledged data preparation system.

Primary keys (PKs) and foreign keys (FKs) are important elements of relational schemata in various applications, such as query optimization and data integration. However, in many cases, these constraints are unknown or not documented. Detecting them manually is time-consuming and even infeasible in large-scale datasets. We study the problem of discovering primary keys and foreign keys automatically and propose an algorithm to detect both, namely Holistic Primary Key and Foreign Key Detection (HoPF). PKs and FKs are subsets of the sets of unique column combinations (UCCs) and inclusion dependencies (INDs), respectively, for which efficient discovery algorithms are known. Using score functions, our approach is able to effectively extract the true PKs and FKs from the vast sets of valid UCCs and INDs. Several pruning rules are employed to speed up the procedure. We evaluate precision and recall on three benchmarks and two real-world datasets. The results show that our method is able to retrieve on average 88% of all primary keys, and 91% of all foreign keys. We compare the performance of algor with two baseline approaches that both assume the existence of primary keys.

In the past few years, we have witnessed a rapid development of online social media, from which we can access various short texts. Understanding the topic patterns of these short text is significant. Traditional topic models, like LDA, are not suitable when applied to short text topic analysis due to data sparsity. A lot of efforts have been made to solve this problem. However, there is still significant space to improve the effectiveness of these short text specific methods. In this paper, we proposed a novel word co-occurrence network based method, referred to as biterm pseudo document topic model (BPDTM), which extended the previous biterm topic model(BTM) for short text. We utilized the word co-occurrence network to construct biterm pseudo documents. The proposed model is promising since it represents words with their semantic adjacent biterms and is able to model the corpus-level semantic relation between two words. Besides, BPDTM naturally lengthens the documents, which alleviate the influence for performance exerted by data sparsity. Experiments demonstrated that our model outperformed two baselines, i.e. LDA and BTM, which proved its effectiveness on short text topic modeling task.

The booming development of Online Social Networks (OSNs) provides a novel way of expressing emotions. Research on emotion analysis for tweets in OSNs is becoming increasingly popular in recent years. Traditional emotion analysis only classifies one tweet into a single emotion category. However, in reality, a tweet may belong to several different emotion categories. In the paper, our goal is to predict all emotion labels of each tweet. We use graphic emoticons, punctuation expressions together with a tiny but accurate lexicon to label data and provide a Multi-label Emotion Classification algorithm (MEC) for tweets in Weibo (so called Chinese Twitter). Our method has superior performance to the state-of-the-art method under both single-label and multi-label evaluation measures. We also carried out a case study on Weibo dataset of Malaysia Missing Flight. We came to several meaningful conclusions such as "The outbreak of Anger has a delay after breaking point of Sadness".