Welcome to a massive multimedia extravaganza trip report from Provenance Week held earlier this month June 9 -13.
Provenance Week brought together two workshops on provenance plus several co-located events. It had roughly 65 participants. It’s not a huge event but it’s a pivotal one for me as it brings together all the core researchers working on provenance from a range of computer science disciplines. That means you hear the latest research on the topic ranging from great deployments of provenance systems to the newest ideas on theoretical properties of provenance. Here’s a picture of the whole crew:
Given that I’m deeply involved in the community, it’s going to be hard to summarize everything of interest because…well…everything was of interest, it also means I had a lot of stuff going on. So what was I doing there?
Together with Luc Moreau and Trung Dong Huynh, I kicked off the week with a tutorial on the W3C PROV provenance model. The tutorial was based on my recent book with Luc. From my count, we had ~30 participants for the tutorial.
We’ve given tutorials in the past on PROV but we made a number of updates as PROV is becoming more mature. First, as the audience had a more diverse technical background we came from a conceptual model (UML) point of view instead of starting with a Semantic Web perspective. Furthermore, we presented both tools and recipes for using PROV. The number of tools we now have out for PROV is growing – ranging from conversion of PROV from various version control systems to neuroimaging workflow pipelines that support PROV.
I think the hit of the show was Dong’s demonstration of interacting with PROV using his Prov python module (pypi) and Southampton’s Prov Store.
Papers & Posters
I had two papers in the main track of the International Provenance and Annotation Workshop (IPAW) as well as a demo and a poster.
Manolis Stamatogiannakis presented his work with me and Herbert Bos – Looking Inside the Black-Box: Capturing Data Provenance using Dynamic Instrumentation . In this work, we looked at applying dynamic binary taint tracking to capture high-fidelity provenance on desktop systems. This work solves what’s known as the n-by-m problem in provenance systems. Essentially, it allows us to see how data flows within an application without having to instrument that application up-front. This lets us know exactly which output of a program is connected to which inputs. The work was well received and we had a bunch of different questions both around speed of the approach and whether we can track high-level application semantics. A demo video is below and you can find all the source code on github.
We also presented our work on converting PROV graphs to IPython notebooks for creating scientific documentation (Generating Scientific Documentation for Computational Experiments Using Provenance). Here we looked at how to try and create documentation from provenance that is gathered in a distributed setting and put that together in easy to use fashion. This work was part of a larger kind of discussion at the event on the connection between provenance gathered in these popular notebook environments and that gathered on more heterogeneous systems. Source code again on github.
I presented a poster on our (with Marcin Wylot and Philippe Cudré-Mauroux) recent work on instrumenting a triple store (i.e. graph database) with provenance. We use a long standing technique provenance polynomials from the database community but applied for large scale RDF graphs. It was good to be able to present this to those from database community that we’re at the conference. I got some good feedback, in particular, on some efficiencies we might implement.
I also demoed (see above) the really awesome work by Rinke Hoekstra on his PROV-O-Viz provenance visualization service. (Paper, Code) . This was a real hit with a number of people wanting to integrate this with their provenance tools.
Provenance Reconstruction + ProvBench
At the end of the week, we co-organized with the ProvBench folks an afternoon about challenge tasks and benchmark datasets. In particular, we looked at the challenge of provenance reconstruction – how do you recreate provenance from data when you didn’t track it in the first place. Together with Tom De Nies we produced a number of datasets for use with this task. It was pretty cool to see that Hazeline Asuncion used these data sets in one of her classes where her students used a wide variety of off the shelf methods.
From the performance scores, precision was ok but very dataset dependent and relies on a lot on knowledge of the domain. We’ll be working with Hazeline to look at defining different aspects this problem going forward.
Provenance reconstruction is just one task where we need datasets. ProvBench is focused on gathering those datasets and also defining new challenge tasks to go with them. Checkout this github for a number of datasets. The PROV standard is also making it easier to consume benchmark datasets because you don’t need to write a new parser to get a hold of the data. The dataset I most liked was the Provenance Capture Disparities dataset from the Mitre crew (paper). They provide a gold standard provenance dataset capturing everything that goes on in a desktop environment, plus, two different provenance traces from different kinds of capture systems. This is great for testing both provenance reconstruction but also looking how to merge independent capture sources to achieve a full picture of provenance.
There is also a nice tool to covert Wikipedia edit histories to PROV.
I think I picked out four large themes from provenance week.
- Transparent collection
- Provenance aggregation, slicing and dicing
- Provenance across sources
One issue with provenance systems is getting people to install provenance collection systems in the first place let alone installing new modified provenance-aware applications. A number of papers reported on techniques aimed to make it easier to capture more transparent.
A couple of approaches tackled this for the programming languages. One system focused on R (RDataTracker) and the other python (noWorkflow). I particularly enjoyed the noWorkflow python system as they provided not only transparent capture for provenance systems but a number of utilities for working with the captured provenance. Including a diff tool and a conversion from provenance to Prolog rules (I hope Jan reads this). The prolog conversion includes rules that allow for provenance specific queries to be formulated. (On Github). noWorkflow is similar to Rinke’s PROV-O-Matic tool for tracking provenance in python (see video below). I hope we can look into sharing work on a really good python provenance solution.
An interesting discussion point that arose from this work was – how much we should expose provenance to the user? Indeed, the team that did RDataTracker specifically inserted simple on/off statements in their system so the scientific user could control the capture process in their R scripts.
Tracking provenance by instrumenting the operating system level has long been an approach to provenance capture. Here, we saw a couple of techniques that tried to reduce that tracking to simply launching a system background process in user space while improving the fidelity of provenance. This was the approach of our system Data Tracker and Cambridge’s OPUS (specific challenges in dealing with interposition on the std lib were discussed). Ashish Gehani was nice enough to work with me to get his SPADE system setup on my mac. It was pretty much just a checkout, build, and run to start capturing reasonable provenance right away – cool.
Databases have consistently been a central place for provenance research. I was impressed Boris Glavic’s vision (paper) of a completely transparent way to report provenance for database systems by leveraging two common database functions – time travel and an audit log. Essentially, through the use of query rewriting and query replay he’s able to capture/report provenance for database query results. Talking to Boris, they have a lot it implemented already in collaboration with Oracle. Based on prior history (PostgresSQL with provenance), I bet it will happen shortly. What’s interesting is that his approach requires no modification of the database and instead sits as middleware above the database.
Finally, in the discussion session after the Tapp practice session, I asked the presenters who represented the range of these systems to ballpark what kind of overhead they saw for capturing provenance. The conclusion was that we could get between 1% – 15% overhead. In particular, for deterministic replay style systems you can really press down the overhead at capture time.
Provenance aggregation, slicing and dicing
I think Susan Davidson said it best in her presentation on provenance for crowdsourcing – we are at the OLAP stage of provenance. How do we make it easy to combine, recombine, summarize, and work with provenance. What kind of operators, systems, and algorithms do we need? Two interesting applications came to the fore for this kind of need – crowdsourcing and security. Susan’s talk exemplified this but at the Provenance Analytics event there were several other examples (Huynh et al., Dragon et al).
The other area was security. Roly Perera presented his impressive work with James Cheney on cataloging various mechanisms for transforming provenance graphs for the purposes of obfuscating or hiding sensitive parts of the provenance graph. This paper is great reference material for various mechanisms to deal with provenance summarization. One technique for summarization that came up several times in particular with respect to this domain was the use of annotation propagation through provenance graphs (e.g. see ProvAbs by Missier et al. and work by Moreau’s team.)
Provenance across sources
The final theme I saw was how to connect provenance across sources. One could also call this provenance integration. Both Chapman and the Mitre crew with their Provenance Plus tracking system and Ashish with his SPADE system are experiencing this problem of provenance coming from multiple different sources and needing to integrate these sources to get a complete picture of provenance both within a system and spanning multiple systems. I don’t think we have a solution yet but they both (ashish, chapman) articulated the problem well and have some good initial results.
This is not just a systems problem, it’s fundamental that provenance extends across systems. Two of the cool use cases I saw exemplified the need to track provenance across multiple sources.
The Kiel Center for Marine Science (GEOMAR) has developed a provenance system to track their data throughout their entire organization stemming from data collected on their boats all the way through a data publication. Yes you read that right, provenance gathered on awesome boats! This invokes digital pens, workflow systems and data management systems.
The other was the the recently released US National Climate Change Assessment. The findings of that report stem from 13 different institutions within the US Government. The data backing those findings is represented in a structured fashion including the use of PROV. Curt Tilmes presented more about this amazing use case at Provenance Analytics.
In many ways, the W3C PROV standard was created to help solve these issues. I think it does help but having a common representation is just the start.
I didn’t mention it but I was heartened to see that community has taken to using PROV as a mechanism for interchanging data and for having discussions. My feeling is that if you can talk provenance polynomials and PROV graphs, you can speak with pretty much anybody in the provenance community no matter which “home” they have – whether systems, databases, scientific workflows, or the semantic web. Indeed, this is one of the great things about provenance week, is that one was able to see diverse perspectives on this cross cutting concern of provenance.
Lastly, there seemed to many good answers at provenance week but more importantly lots of good questions. Now, I think as a community we should really expose more of the problems we’ve found to a wider audience.
- It was great to see the interaction between a number of different services supporting PROV (e.g. git2prov.org, prizims , prov-o-viz, prov store, prov-pings, PLUS)
- ProvBench on datahub – thanks Tim
- DLR did a fantastic job of organizing. Great job Carina, Laura and Andreas!
- I’ve never had happy birthday sung to me at by 60 people at a conference dinner – surprisingly in tune – Kölsch is pretty effective. Thanks everyone!
- Stefan Woltran’s keynote on argumentation theory was pretty cool. Really stepped up to the plate to give a theory keynote the night after the conference dinner.
- Speaking of theory, I still need to get my head around Bertram’s work on Provenance Games. It looks like a neat way to think about the semantics of provenance.
- Check out Daniel’s trip report on provenance week.
- I think this is long enough…..