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Skluma: A Statistical Learning Pipeline for Taming Unkempt Data Repositories


Abstract and Figures

Scientists' capacity to make use of existing data is predicated on their ability to find and understand those data. While significant progress has been made with respect to data publication, and indeed one can point to a number of well organized and highly utilized data repositories, there remain many such repositories in which archived data are poorly described and thus impossible to use. We present Skluma---an automated system designed to process vast amounts of data and extract deeply embedded metadata, latent topics, relationships between data, and contextual metadata derived from related documents. We show that Skluma can be used to organize and index a large climate data collection that totals more than 500GB of data in over a half-million files.
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Skluma: A Statistical Learning Pipeline for Taming Unkempt
Data Repositories
Paul Beckman, Tyler J. Skluzacek, Kyle Chard, and Ian Foster
Computation Institute
University of Chicago and Argonne National Laboratory
Chicago, IL 60637
Scientists’ capacity to make use of existing data is predicated on
their ability to nd and understand those data. While signicant
progress has been made with respect to data publication, and indeed
one can point to a number of well organized and highly utilized
data repositories, there remain many such repositories in which
archived data are poorly described and thus impossible to use. We
present Skluma—an automated system designed to process vast
amounts of data and extract deeply embedded metadata, latent
topics, relationships between data, and contextual metadata derived
from related documents. We show that Skluma can be used to
organize and index a large climate data collection that totals more
than 500GB of data in over a half-million les.
Information systems Data cleaning
Mediators and data
data wrangling, statistical learning, metadata extraction, data inte-
ACM Reference format:
Paul Beckman, Tyler J. Skluzacek, Kyle Chard, and Ian Foster. 2017. Skluma:
A Statistical Learning Pipeline for Taming Unkempt Data Repositories. In
Proceedings of SSDBM ’17, Chicago, IL, USA, June 27-29, 2017, 4 pages.
Meaningless le names. Limited documentation. Unlabeled columns.
Numerically encoded null values. Multifarious le extensions. Sci-
entists live this nightmare daily as they seek to discover and use
publicly available data stored in heterogeneous data repositories.
As the rate of data production explodes (e.g., due to higher reso-
lution instruments and massive sensor networks), clear, uniform
documentation and organization of data are often neglected. Many
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eorts have focused on standardizing data naming and organiza-
tion models within and across research groups [
]. We, and
others, have developed workow-oriented data publication sys-
tems that impose requirements on organization and metadata [
While there are clear success stories with respect to structured data
repositories [
], repositories often become dumping grounds for
poorly described data [
]. We postulate that new methods based in
statistical learning are needed to make sense of the vast amounts
of data already published to existing repositories. To this end, we
propose an automated pipeline (Skluma) and associated models and
methods that allows us to process and classify disorderly data, striv-
ing to provide the metadata necessary to enable complex querying
of previously incomprehensible scientic data repositories.
Skluma is organized around a three-stage pipeline: crawl hetero-
geneous data repositories, extract metadata on each le’s content,
and contextualize data in order to enrich, augment, and improve the
accuracy of existing metadata. Throughout these stages, Skluma
accumulates and renes metadata through a number of information
extraction and statistical learning models.
To illustrate the value of Skluma we have used it to organize and
index the contents of the United States Department of Energy’s Car-
bon Dioxide Information Analysis Center (CDIAC) data store [
This lesystem-based repository contains over a half-million les
of diverse types, structures, and sizes, distributed among twelve
grab-bag ‘pub’ top-level directories. Many les are compressed,
named according to undocumented systems, organized in arbitrary
hierarchies, and stored in nonstandard formats. Furthermore, there
are duplicate les both within and between directories, and sci-
entically useless les (e.g., Windows Installers, shortcuts, empty
zipped directories). CDIAC contains more than 150 dierent le
extensions, making the implementation of type- or format-specic
metadata extractors infeasible. Figure 1 shows the distribution of
le types. CDIAC is illustrative of a common problem in science:
while researchers may work hard to address problems of veriabil-
ity and reproducibility, these considerations are easily obscured or
lost by publishing disorganized and undocumented data. Skluma
works to reclaim this missing context and restore the usefulness of
disarrayed repositories.
The remainder of the paper is organized as follows: Section 2
provides a brief overview of past methods for database and repos-
itory cleaning. Section 3 outlines our pipeline and presents test
results. Section 4 describes our planned demonstration of Skluma.
Section 5 outlines future work and considerations. Finally, Section
6 provides a full analysis of Skluma.
SSDBM ’17, June 27-29, 2017, Chicago, IL, USA Paul Beckman, Tyler J. Skluzacek, Kyle Chard, and Ian Foster
Figure 1: CDIAC le extension distribution: Counts for the
35 most common le extensions in CDIAC.
Skluma follows a long line of attempts to organize and gain insight
into highly disorganized data. Related work on geospatial attribute
recognition has paired simple rule-based analysis with the use of
support vector machines (SVMs) in order to predict attributes in a
broad range of geospatial datasets [
]. Current methods focus
on well-structured data with higher consistency than data sources
like CDIAC.
Skluma is also not the rst to provide a scalable solution to collect
raw datasets and extract metadata from them. Pioneering research
on data lakes has developed methods for extracting standard meta-
data from nonstandard le types and formats [
]. Recently the data
lake has been adapted to standardize and extract metadata from
strictly-geospatial datasets [
]. Normally data lakes have some sort
of institution-specic target for which they are optimized, whether
they primarily input transactional, scientic, or networked data.
Skluma is optimized for data without any standardization guar-
antees, providing information on related, relevant data and their
Finally, Skluma supplements existing work that cleans and la-
bels data using context features. Data Civilizer [
] accounts for
proximate les in data warehouses by building and interpreting
linkage graphs. Others have used context as a means to determine
how certain metadata might be leveraged to optimize performance
in a specic application or API [
]. Skluma collects and analyzes
context metadata in order to allow research scientists to nd, query,
and download related datasets that aid their scientic eorts.
Skluma implements a three-stage pipeline: (1) crawling, (2) meta-
data extraction, and (3) contextualization.
3.1 Crawling
Skluma’s rst task is to catalog the data in order to understand its
organization and scope. While crawling, Skluma extracts general
le-level metadata, such as le name, path, size, a checksum, exten-
sion and MIME type [
] for each le. Given the wide variety of
data access protocols (e.g., HTTP, FTP, GridFTP) oered by data
repositories, Skluma is designed with a modular crawling architec-
ture in which dierent crawler implementations can be used. In the
case of CDIAC, we used FTP and HTTP crawlers. As a result of the
crawling phase Skluma creates a JSON document that stores basic
system metadata about each le. This JSON le is stored, modied,
and appended to throughout the Skluma pipeline.
In order to extract metadata from within les, Skluma requires a
mutable le system to execute decompression if necessary and to
store the resulting les. As data repositories often do not provide
such resources, the Skluma crawler mirrors data temporarily to a
high-performance storage environment, where metadata extraction
can be performed. In the CDIAC scenario, we use Globus [
] to
transfer all CDIAC data temporarily to Petrel [
], a 1 PB storage
system housed at Argonne National Laboratory.
3.2 Metadata Extraction
The second pipeline phase iterates over all les discovered during
the crawling phase and extracts metadata from each le based on its
content. We leverage a suite of modular extraction tools to obtain
general, le, and domain-specic metadata. As these extractors
can require signicant compute resources, we use Jetstream as a
scalable platform on which to execute arbitrary extraction tools on
the data. Petrel serves as a performant storage system from which
we can rapidly access data for processing on Jetstream.
The end goal of extracted metadata is to facilitate queries over a
repository with eld-specic predicates (e.g., “return all CDIAC les
with temperature values greater than 20 C”). To this end, Skluma’s
metadata extractors address two main types of data: containerized
and column-formatted. Containerized data formats like NetCDF
already include much of the metadata necessary for query construc-
tion accessible in standard formats and via standard interfaces. In
this case, Skluma simply reformats this information and copies it
into the metadata le. For any le that can be parsed as column-
formatted, we calculate the min, max, and average for numerical
columns. In addition, we collect all available headers.
In order for these aggregates to have any meaning, however,
encoded null values must be detected and (typically) skipped. Oth-
erwise, to use a CDIAC example, we may nd that a scientist records
-999 to indicate that no temperature measurement was taken, lead-
ing to a le with an average temperature of -437 C. Thus Skluma em-
ploys a supervised learning model to infer null values and exclude
them from aggregate calculations. We use a
-nearest neighbor
Skluma: A Statistical Learning Pipeline for Taming Unkempt Data Repositories SSDBM ’17, June 27-29, 2017, Chicago, IL, USA
Figure 2: PCA visualization of null values: Cutout shows dense region of the feature space at 2500x zoom.
classication algorithm, using the average, the three largest values
and their dierences, and the three smallest values and their dier-
ences as features for our model. By taking a classication rather
than regression-based approach, Skluma selects from a preset list
of null values, which avoids discounting real experimental outliers
recorded in the data itself.
Figure 2
provides a PCA visualization
of the clustering of null values in the feature space.
When trained and tested by cross-validation on a labeled test set
of 4682 columns from 335 unique les, our model achieved accuracy
0.991, precision 0.989, and recall 0.961, where precision and recall
are calculated by macro-averaging over classiers.
At this point in the pipeline the accuracy of the aggregate values
has been improved to better reect the existing data. However, if the
column header is not provided in the le itself, these values provide
very little information that can be used for query or discovery.
This is a common occurrence in CDIAC data. Files often contain
only numerical values, whereas column name information is in a
separate free-text README le in a nearby directory. The problem
of associating unlabelled data columns with headers is addressed
by the third portion of the Skluma pipeline.
3.3 Contextualization
The relationships and similarities between les within and outside
a data repository can provide valuable information regarding the
nature of these les. Specically, topic labels on free-text documents
can serve as valuable context to describe nearby undocumented data.
Skluma employs a topic mixture model based on Latent Dirichlet
Allocation [
]. Our model is made up of three steps. First, we train
our model on Web of Science (WoS) abstracts. Next, we use this
model to generate the topic distribution of each free-text README
or documentation le in the data repository, which is the nite
mixture over an underlying set of topics derived from the model.
Finally, we model all data les in the repository as themselves nite
mixtures of the topic distributions of the surrounding labelled les.
We calculate the topic mixture of a given le as a linear combination
w1d1+w2d2+... +wndn
of the topic distributions
d1, . . ., dn
of all
nearby tagged free-text documents within a distance threshold. The
w1, . . ., wn
are inversely proportional to the distance within
the repository of the data le to the tagged text document. The
distance metric we use is dependent on the type of repository being
considered. For directory-structured le systems like CDIAC, we
use the number of directory changes that must be done in order to
reach one le from another. A simple illustration of this model is
shown in
Figure 3
. We then execute these steps by submitting a
series of jobs to the Cloud Kotta [3] platform.
This model serves three purposes. Firstly, it adds an additional
queryable attribute to the metadata, enabling searches by probable
topic. Secondly, it can act as a basis for selecting specialized meta-
data extraction tools for classied les. Finally, it may be used as
a feature for a statistical learning model used to predict missing
column headers. We discuss this nal prospect in Section 5.
The demonstration of Skluma involves executing our pipeline on a
small subset of CDIAC. Specically, we will demonstrate inference
of attributes and null-values, tagging les with topics via analy-
sis of their proximate READMEs, and extracting metadata such
that the les are queryable by both their content and topic-context.
Furthermore, we will query over Skluma’s resulting metadata by
using a simple, web-based search GUI built atop ElasticSearch. The
SSDBM ’17, June 27-29, 2017, Chicago, IL, USA Paul Beckman, Tyler J. Skluzacek, Kyle Chard, and Ian Foster
Figure 3: LDA label mixture model: Files are colored accord-
ing to the relative weight of the surrounding topics; red rep-
resents “Atmospheric Science” and blue “Oceanography.
takeaway from the demo should be as follows: despite the reposi-
tory’s disorganized structure and content, Skluma is able to provide
informative metadata that can be used to facilitate data discovery.
For column-structured data, one important step towards facilitating
data discovery is the inference of column headers in unlabelled
data. At this juncture, Skluma can produce accurate aggregates by
removing null values and provide topic-based context for header-
less les. We intend to develop additional statistical learning models
that leverage these informative features in conjunction with further
natural language processing techniques. These approaches may
allow us to use free-text documentation les to predict specic
column headers in nearby les, which would greatly increase the
amount of previously unsearchable data that can be indexed for
querying and discovery.
Beyond column-formatted data, we will augment the metadata
we collect from semi-structured and unstructured les. To do so, we
have begun exploring variants of other schema-extraction paradigms
] as an initial step in the pipeline. Providing further insight
into unstructured data les will broaden the coverage of Skluma’s
derived metadata, moving towards more comprehensive data dis-
Skluma’s three-step pipeline supports crawling, metadata extrac-
tion, and contextualization, working to provide the metadata nec-
essary for a data querying environment for scientists. We employ a
number of statistical learning models in order to determine the char-
acteristics of and relationships between les despite irregularities,
missing elds, and haphazard organization. The development and
implementation of this class of automated information extraction
methods has the potential to greatly expand the quantity of usable
scientic data. We continue to expand and rene Skluma in order
to better convert unkempt data repositories into clear, searchable
resources that can propel novel research and analysis.
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