Every day, tens of thousands of Amazon customers message Amazon's selling partners inquiring about orders, products and services. Unfortunately, we have observed over time that buyers may send unsolicited and often times mal intended messages to sellers through the buyer-seller messaging (BSM) service. Although the BSM service gives the ability to sellers to report such messages, most of them do not make use of this feature. Hence, collecting training and testing data with clean labels to build machine learning models in order to proactively block unsolicited messages and help prevent and mitigate losses for Amazon is extremely challenging. To address this problem we propose ClusterClean, an algorithm that automatically cleans data labels with little to no human effort. ClusterClean has the ability to a) accurately infer the labels of unlabeled data points based on an initial small labeled set and b) detect new data patterns such as incoming unobserved spam attacks by soliciting feedback from users to decide on their label. Experiments on approximately 150,000 real messages from Amazon customers showed that ClusterClean can accurately clean the labels of these messages in a few minutes, drastically reducing the human effort and time spent on this task.

In this paper, we argue that database systems be augmented with an automated data exploration service that methodically steers users through the data in a meaningful way. Such an automated system is crucial for deriving insights from complex datasets found in many big data applications such as scientific and healthcare applications as well as for reducing the human effort of data exploration. Towards this end, we present AIDE, an Automatic Interactive Data Exploration framework that assists users in discovering new interesting data patterns and eliminate expensive ad-hoc exploratory queries. AIDE relies on a seamless integration of classification algorithms and data management optimization techniques that collectively strive to accurately learn the user interests based on his relevance feedback on strategically collected samples. We present a number of exploration techniques as well as optimizations that minimize the number of samples presented to the user while offering interactive performance. AIDE can deliver highly accurate query predictions for very common conjunctive queries with small user effort while, given a reasonable number of samples, it can predict with high accuracy complex disjunctive queries. It provides interactive performance as it limits the user wait time per iteration of exploration to less than a few seconds.

Data analysts often engage in data exploration tasks to discover interesting data patterns, without knowing exactly what they are looking for. Such exploration tasks can be very labor-intensive because they often require the user to review many results of ad-hoc queries and adjust the predicates of subsequent queries to balance the tradeoff between collecting all interesting information and reducing the size of returned data. In this demonstration we introduce AIDE , a system that automates these exploration tasks. AIDE steers the user towards interesting data areas based on her relevance feedback on database samples, aiming to achieve the goal of identifying all database objects that match the user interest with high efficiency. In our demonstration, conference attendees will see AIDE in action for a variety of exploration tasks on real-world datasets.

Interactive Data Exploration (IDE) is a key ingredient of a diverse set of discovery-oriented applications, including ones from scientific computing and evidence-based medicine. In these applications, data discovery is a highly ad hoc interactive process where users execute numerous exploration queries using varying predicates aiming to balance the trade-off between collecting all relevant information and reducing the size of returned data. Therefore, there is a strong need to support these human-in-the-loop applications by assisting their navigation in the data to find interesting objects.