Most scientific and modern applications generate—in addition to the base data—valuable annotations and metadata information at unprecedented scale and complexity. Such annotations warrant the need for advanced annotation management techniques that not only propagate the raw annotations to end-users, but also mine, summarize, and extract useful knowledge from them. Towards this goal, we proposed the InsightNotes system, the first summary-based annotation management engine in relational databases [22]. InsightNotes relies on creating concise representations of the raw annotations, called annotation summaries. InsightNotes addresses several unique challenges related to the maintenance, propagation, and zooming of these summaries. However, a key limitation is that the annotation summaries are treated as propagate-only (report-only) objects that cannot be directly queried or manipulated. This limitation hinders higher-level applications from applying complex processing over both the base data and its attached annotation summaries even within a single query. In this paper, we propose new extensions to InsightNotes for treating the annotation summaries as first-class citizens. We address the challenges of: (1) Developing new manipulation functions and query operators specific for the annotation summaries, (2) Designing summary-based index structures and access methods for efficient retrieval and predicate evaluation, and (3) Extending the query optimizer to optimize queries accessing both the data and the annotation summaries. The proposed extensions not only make it feasible to natively query and manipulate the annotation summaries, but also achieve more than two orders of magnitude speedup in query evaluation.

Annotation management and data curation has been extensively studied in the context of relational databases. However, existing annotation management techniques share a common limitation, which is that they are all passive engines, i.e., they only manage the annotations obtained from external sources such as DB admins, domain experts, and curation tools. They neither learn from the available annotations nor exploit the annotations-to-data correlations to further enhance the quality of the annotated database. Delegating such crucial and complex tasks to end-users---especially under large-scale databases and annotation sets---is clearly the wrong choice. In this paper, we propose the Nebula system, an advanced and proactive annotation management engine in relational databases. Nebula complements the state-of-art techniques in annotation management by learning from the available annotations, analyzing their content and semantics, and understanding their correlations with the data. And then, Nebula proactively discovers and recommends potentially missing annotation-to-data attachments. We propose context-aware ranking and prioritization of the discovered attachments that take into account the relationships among the data tuples and their annotations. We also propose approximation techniques and expert-enabled verification mechanisms that adaptively maintain high-accuracy predictions while minimizing the experts' involvement. Nebula is realized on top of an existing annotation management engine, and experimentally evaluated to illustrate the effectiveness of the proposed techniques, and to demonstrate the potential gain in enhancing the quality of annotated databases.

In this paper, we demonstrate the FusionDB system; an extended relational database engine for managing conflicts in small-science databases. In small sciences, groups---each consists of few scientists---may share and exchange parts of their own databases among each other to foster collaboration. The goal of such sharing, especially when done at early stages of the discovery process, is not to build a warehouse or a unified schema, instead the goal is to compare and verify results, detect and assess conflicts, and possibly modify or re-design the discovery process. FusionDB is designed to meet the requirements and address the challenges of such sharing model. We will demonstrate the key functionalities of FusionDB including: (1) Detecting conflicts using a rule-based model over heterogeneous schemas, (2) Assessing conflicts and providing probabilistic estimates for values' correctness, (3) Extended querying capabilities in the presence of conflicts, and (4) Providing curation operations to help scientists resolve and investigate conflicts according to different priorities. FusionDB is realized on top of PostgreSQL DBMS.