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Challenges of Capturing System Activity
A key aspect of the work I am doing for Indaleko is to “capture system activity” so that it can be used to form “activity contexts” that can then be used to inform the process of finding relevant information. As part of that, I have been working through the work of Daniela Vianna. While I have high-level descriptions of the information she collected and used, I need to reconstruct this. She collects data from a variety of sources. The most common source of information comes from web APIs to services such as Google and Facebook. In addition, she also uses file system activity information.
Since my background is file systems, I decided to start on the file system activity front first. Given that I’ve been working with Windows for three decades now, I decided to leverage my understanding of Windows file systems to collect such information. One nice feature of the NTFS file system on Windows is its support for a form of activity log known as the “USN Journal.” Of course, one of my handicaps is that I am used to using the native operating system API, not the libraries that are implemented on top of it. This is because when building file systems on Windows I have always been interested in testing the full kernel file systems interface. While there are a few specific features that cannot be exercised with just applications, there are still a number of interfaces that cannot be tested using the typical Win32 API that can be tested using the native API. In recent years the number of features that have been hidden from the Win32 API has continued to decrease, which has diminished the need to use the native API. I just haven’t had any strong need to learn the Win32 API – why start now?
I decided the model I want to use is a service that pulls data from the USN journal and converts it into a format suitable for storing in a MongoDB database. I decided to go with Mongo because that is what Vianna used for her work. The choice at this point is somewhat arbitrary but MongoDB makes sense because it tends to work well with semi-structured data, which is what I will be handling.
Similarly, I decided that I’d write my service for pulling USN Journal data from the NTFS file system(s) in C# since I have written some C# in the past, it makes doing some of the higher level tasks I have much easier, and is well-supported on Windows. I have made my repository public though I may restructure and/or rename it at some point (currently I call it CSharpToNativeTest because I was trying to invoke the native API as unmanaged code from C#). The most common approach to this is to utilize a specific mechanism (the “PInvoke” mechanism) but after a bit of trial-and-error I decided I wanted something that would be easier for me to debug, so instead of pulling the native routine directly from ntdll.dll I load it from my own DLL (written in C) and that then invokes the real native call. This allows me to see how data is being marshaled and delivered to the C language wrapper. I also tried to make the native API “more C# friendly.” I am sure it could be more efficient, but I wanted to support a model that I could extend and hopefully it will be easier to make it more efficient should that prove necessary.
One thing I did was to script the conversion of all the status values in ntstatus.h into a big C# enum type. The benefit of this is that when debugging I can automatically see the mnemonic name of the status code as well as its numeric value. I then decided to provide the layer needed to map the various volume names used on Windows around, with device names, device IDs, and symbolic links (drive letters) that can be mapped. While I have not yet added it, I wrote things so that it should be fairly straight-forward to add a background thread which wakes up when devices arrive or disappear. As I have noted before “naming is hard.” This is just one more example of the flexibility and challenges with aliasing and naming.
Finally, I turned my attention to the USN journal. I found some packages for decoding USN journal entries; most were written to parse the data from the drive, while a few managed dynamic access. Since I want this to be a service that monitors the USN journal and keeps adding information into the database, I decided to write C# code to use the API for retrieving that information. At this point, what I have is the ability to scan all the volumes on the machine – even if they do not have drive letters – and query them to see if they support a USN journal. I do this properly – I query the file system attributes (using the NtQueryVolumeInformationFile native API) and check if the bit showing USN journal support is marked. I do not use the file system name, an approach I’ve always considered to be a hack, especially since I have been in the habit of writing file systems that support NTFS features, including named data streams, extended attributes, and object IDs. In fact, the ReFS file system on Windows also supports USN journals, so I’m not just being my usual pedantic developer self in this instance.
At this point, I am able to identify volumes that support USN journals, open them and find out if USN is turned on (it is by default on the system volume, which is almost always the “C:” drive, though I enjoy watching things break when I configure a system to use some other drive letter.) I then extract the information and convert it to in-memory records. At the moment I just have it wait a few seconds and pull the newest records, but my plan is to evolve this into a service that I can run and it can keep pulling data and pushing it into my MongoDB instance.
At this point, I realized I do not really know that much about MongoDB so I have decided to start learning a bit more about it. Of course, I don’t want to be a MongoDB expert, so I also have been looking more carefully at Daniela Vianna’s work, trying to figure out what her data might have looked like and think about how I’m going to merge what she did into what I am doing. This is actually exciting because it means I’m starting to think of what we can do with this additional information.
This afternoon I had a great conversation with one of my PhD supervisors about this and she was making a couple of suggestions about ways to consume this data. That she was suggesting things I’d also added to my list was encouraging. What are we thinking:
- We can consider using “learned index structures” as we begin to build up data sets.
- We can use techniques such as Google BERT to facilitate dealing with the API data that Vianna’s work used. I pointed out that the challenges of APIs that Vianna pointed out are similar to languages: they have meaning and those meanings can be expressed in multiple ways.
- The need for being able to efficiently find things is growing rapidly. She was explaining some work that indicates our rate of data growth is outstripping our silicon capabilities. In other words, there is a point at which “brute force search” becomes impractical. I liked this because it suggests what we are seeing with our own personal data is a leading indicator of the larger problem. This idea of storing the meta-data independent of the data is a natural one in a world where the raw information is too abundant for us to just go looking for an item of interest.
So, my work continues, mostly mundane and boring, but there are some useful observations even at this early stage. Now to figure out what I want the data in my database to look like and start storing information there. Then I can go figure out what I did right, what I did wrong, and how to improve things.
Aside: one interesting aspect of the BERT work was their discussion of “transducers.” This reminded me of Gifford’s Semantic File System work, where he used transducers to suck out semantic information from existing files.
Better Finding: Combine Semantic and Associative Context with Indaleko
Last month I presented my thesis proposal to my PhD committee. My proposal doesn’t mean that I am done, rather it means that I have more clearly identified what I intend to make the focus of my final research.
It has certainly taken longer to get to this point than I had anticipated. Part of the challenge is that there is quite a lot of work that has been done previously around search and semantic context. Very recent work by Daniela Vianna relates to the use of “personal digital traces” to augment search. It was Dr. Vianna’s work that provided a solid theoretical basis for my own proposed work.
Our computer systems collect quite an array of information, not only about us but also about the environment in which we work.
In 1945 Vannevar Bush described the challenges to humans of finding things in a codified system of records. His observations continue to be insightful more than 75 years later:
Our ineptitude in getting at the record is largely caused by the artificiality of systems of indexing. When data of any sort are placed in storage, they are filed alphabetically or numerically, and information is found (when it is) by tracing it down from subclass to subclass. It can be in only one place, unless duplicates are used; one has to have rules as to which path will locate it, and the rules are cumbersome. Having found one item, moreover, one has to emerge from the system and re-enter on a new path.
The human mind does not work that way. It operates by association. With one item in its grasp, it snaps instantly to the next that is suggested by the association of thoughts, in accordance with some intricate web of trails carried by the cells of the brain. It has other characteristics, of course; trails that are not frequently followed are prone to fade, items are not fully permanent, memory is transitory. Yet the speed of action, the intricacy of trails, the detail of mental pictures, is awe-inspiring beyond all else in nature.
I find myself returning to Bush’s observations. Those observations have led me to ask if it is possible for us to build systems that get us closer to this ideal?
My thesis is that collecting, storing, and disseminating information about the environment in which digital objects are being used provides us with new context that enables better finding.
So, my proposal is about how to collect, store, and disseminate this type of external contextual information. I envision combining this with existing data sources and indexing mechanisms to allow capturing activity context in which digital objects are used by humans. A systems level service that can do this will then enable a broad range of applications to exploit this information to reconstruct context that is helpful to human users. Over my next several blog posts I will describe some ideas that I have with what I envision being possible with this new service.
The title of my proposal is: Indaleko: Using System Activity Context to Improve Finding. One of the key ideas from this is the idea that we can collect information the computer might not find particularly relevant but the human user will. This could be something as simple as the ambient noise in the user’s background (“what music are you listening to?” or “Is your dog barking in the background”) or environmental events (“it is raining”) or even personal events (“my heart rate was elevated” or “I just bought a new yoga mat”). Humans associate things together – not in the same way, nor the same specific elements – using a variety of contextual mechanisms. My objective is to enable capturing data that we can then use to replicate this “associative thinking” that helps humans.
Ultimately, such a system will help human users find connections between objects. My focus is on storage because that is my background: in essence, I am interested in how the computer can extend human memory without losing the amazing flexibility of that memory to connect seemingly unrelated “things” together.
In my next several posts I will explore potential uses for Indaleko.
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