In this paper, we explore the artificial generation of typographical errors based on real-world statistics. We first draw on a small set of annotated data to compute spelling error statistics. These are then invoked to introduce errors into substantially larger corpora. The generation methodology allows us to generate particularly challenging errors that require context-aware error detection. We use it to create a set of English language error detection and correction datasets. Finally, we examine the effectiveness of machine learning models for detecting and correcting errors based on this data.

Debugging data processing logic in Data-Intensive Scalable Computing (DISC) systems is a difficult and time consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result programmers spend countless hours collecting evidence ( e.g. , from log files) and performing trial and error debugging. To aid this effort, we built Titian , a library that enables data provenance ---tracking data through transformations---in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds---orders-of-magnitude faster than alternative solutions---while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.

The ZEBRA system, which is part of the VisualHarness platform for managing heterogeneous data, supports three types of access to distributed image repositories: keyword based, attribute based, and image content based. A user can assign different weights (relative importance) to each of the three types, and within the last type of access, to each of the image properties. The image based access component (IBAC) supports access based on computable image properties such as those based on spatial domain, frequency domain or statistical and structural analysis. However, it uses a novel black box approach of utilizing a Visual Information Retrieval (VIR) engine to compute corresponding metadata that is then independently managed in a relational database to provide query processing involving image features and information correlation. That is, one overcomes the difficulties in using the feature vectors that are proprietary to a VTR engine, as one does not require any knowledge of the internal representation or format of the image feature used by a VIR engine.