Enterprises are captivated by the promise of using big data to develop new products and services that provide new insights into their customers and businesses. However, there are signi cant challenges leveraging data across heterogeneous data stores, including making those data accessible and usable by non-experts. We designed a novel system called Helix which employs a combination of userdriven and automated techniques to bootstrap the building of a uni ed semantic model over virtualized data. Such a uniform semantic model allows users to query across data stores transparently, without needing to navigate a maze of data silos, data formats, and query languages. In this poster, we discuss a speci c aspect of Helix: the method by which it facilitates ontology alignment. Such alignments are very noisy and manually xing the issues is a laborious process, especially when all the work must be done prior to putting the system into use. Instead, Helix proposes to engage users progressively in the process of ontology alignment through the course of their everyday use of the system. We do this by framing ontology alignment as a guided data exploration and integration task. Helix provides a uniform user interface for heterogeneous data exploration and linking. This interface abstracts the underlying di erences among data stores and data representations, with the goal of allowing the user to focus on their task rather than the technology. This interface engages users in the data alignment process through three key features: 1. Guided navigation - search and navigate to locate results of interest, assisted by suggestions based on semantic and schematic links 2. Saving results - save results of interest and share those results with others 3. Guided linking - users select two saved results and Helix guides them through ontology alignment. The resulting linked data can be saved, shared, and used in future links. (see Figure 1)
Introduction
Through this process, users are creating ontology alignments by nding data of interest, aligning it, and saving/sharing what they nd useful. In this way,
Ellis et al. everyone who uses Helix is contributing to the alignment of the underlying data sources and can leverage the work of others.
Currently, sharing happens by one user explicitly sending saved results to another. However, we are building a recommender system that will automatically show relevant results to users through the course of their work, allowing them to more readily reuse ontology alignments.
Previous work has proposed systems that perform analysis and
\pay-as-yougo" integration in speci c domains using semantic technologies [
This work is also related to research on making it easier for non-expert users
to query standard database management systems, particularly those that take
an exploratory search approach [