Delmar B. Davis

A Multi-level Funneling Approach to Data Provenance Reconstruction

When data are retrieved from a file storage system or the Internet, is there information about their provenance (i.e., their origin or history)? It is possible that data could have been copied from another source and then transformed. Often, provenance is not readily available for data sets created in the past. Solving such a problem is the motivation behind the 2014 Provenance Reconstruction Challenge. This challenge is aimed at recovering lost provenance for two data sets: one data set (WikiNews articles) in which a list of possible sources has been provided, and another data set (files from GitHub repositories) in which the file sources are not provided. To address this challenge, we present a multi-level funneling approach to provenance reconstruction, a technique that incorporates text processing techniques from different disciplines to approximate the provenance of a given data set. We built three prototypes using this technique and evaluated them using precision and recall metrics. Our preliminary results indicate that our technique is capable of reconstructing some of the lost provenance.

Data Provenance for Multi-Agent Models

Multi-agent simulations are useful for exploring collective patterns of individual behavior in social, biological, economic, network, and physical systems. However, there is no provenance support for multi-agent models (MAMs) in a distributed setting. To this end, we introduce ProvMASS, a novel approach to capture provenance of MAMs in a distributed memory by combining inter-process identification, lightweight coordination of in-memory provenance storage, and adaptive provenance capture. ProvMASS is built on top of the Multi-Agent Spatial Simulation (MASS) library, a framework that combines multi-agent systems with large-scale fine-grained agent-based models, or MAMs. Unlike other environments supporting MAMs, MASS parallelizes simulations with distributed memory, where agents and spatial data are shared application resources. We evaluate our approach with provenance queries to support three use cases and performance measures. Initial results indicate that our approach can support various provenance queries for MAMs at reasonable performance overhead.

Data Provenance for Agent-Based Models in a Distributed Memory

Agent-Based Models (ABMs) assist with studying emergent collective behavior of individual entities in social, biological, economic, network, and physical systems. Data provenance can support ABM by explaining individual agent behavior. However, there is no provenance support for ABMs in a distributed setting. The Multi-Agent Spatial Simulation (MASS) library provides a framework for simulating ABMs at fine granularity, where agents and spatial data are shared application resources in a distributed memory. We introduce a novel approach to capture ABM provenance in a distributed memory, called ProvMASS. We evaluate our technique with traditional data provenance queries and performance measures. Our results indicate that a configurable approach can capture provenance that explains coordination of distributed shared resources, simulation logic, and agent behavior while limiting performance overhead. We also show the ability to support practical analyses (e.g., agent tracking) and storage requirements for different capture configurations.

BrainGrid+Workbench: High-performance/high-quality neural simulation

Availability of affordable hardware that in effect enables desktop supercomputing has enabled more ambitious neural simulations driven by more complex software. However, this opportunity comes with costs, in terms of long learning curves to take advantage of the performance possibilities of idiosyncratic, architecturally heterogenous hardware and decreasing ability to be confident in the quality of simulation results. This paper describes a new neural simulation and software/data provenance framework that reduces the difficulty of taking full advantage of GPU computing and increases investigator confidence that simulations results are valid.

Improving data provenance reconstruction via a multi-level funneling approach

The ease with which data can be created, copied, modified, and deleted over the Internet has made it increasingly difficult to determine the source of web data. Data provenance, which provides information about the origin and lineage of a dataset, assists in determining its genuineness and trustworthiness. Several data provenance techniques record provenance when the data is created or modified. However, many existing datasets have no recorded provenance. Provenance Reconstruction techniques attempt to generate an approximate provenance in these datasets. Current reconstruction techniques require timing metadata to reconstruct provenance. In thats paper, we improve our multi-funneling technique, which combines existing techniques, including topic modeling, longest common subsequence, and genetic algorithm to achieve higher accuracy in reconstructing provenance without requiring timing metadata. In addition, we introduce novel funnels that are customized to the provided datasets, which further boosts precision and recall rates. We evaluated our approach with various experiments and compare the results of our approach with existing techniques. Finally, we present lessons learned, including the applicability of our approach to other datasets.

WIP: Provenance Support for Interdisciplinary Research on the North Creek Wetlands

Researchers working in the North Creek Wetlands are faced with the task of gathering and managing large amounts of data. This interdisciplinary group of researchers also require data provenance to ensure the integrity of their collected data. Currently, they record notes in the wetlands with pen and paper, transcribe these notes, and combine them with the other data they collected, such as spatial and image data. This process is error-prone and can be time consuming. Current provenance techniques also do not focus on supporting provenance from data collection to data processing and do not provide a flexible means of capturing provenance across different software tools and platforms. Our technique and our tool support, ProvEco System, leverages various technologies such as mobile devices, an off-the-shelf geographic information system software, an enterprise-level search facility, and open data standards to provide an interoperable provenance system. Our preliminary results indicate that our data collection application is easy to use and can assist with automatically reconstructing provenance for image files. We also offer generalizable lessons learned for developing a provenance system in other contexts.