On Automating Decentralized Multi-Step Service Combination

Information on the Web is heterogeneous and available in constantly increasing quantities. Consequently, there are numerous, partly redundant data analytics services, each optimized for data with certain characteristics. Often, analytics tasks require multiple services to be pipelined to find a solution, where combinations of exchangeable services for single steps might outperform one-service-predictions. This work proposes a Multi-Agent System (MAS) perception of prior setting, where decentralized agents are considered to manage services, having to coordinate their decisions to find a consensus. We, first, propose a supervised method for service accuracy estimation and, therefore, exploit locality-sensitive features of training data. Given a committee of services managed by agents, we develop coordination strategies to handle conflicting confidences and reduce erroneous predictions due to service correlation. We evaluate our approach with Named Entity Recognition (NER)- and Named Entity Disambiguation (NED) services on text corpora with heterogeneous characteristics (i.e. news articles and tweets). Our empirical results improve the out-of-the-box performance of the original services.