Overview of Data Exploration Techniques
Stratos Idreos
Harvard University
stratos@seas.harvard.edu
Olga Papaemmanouil
Brandeis University
olga@cs.brandeis.edu
Surajit Chaudhuri
Microsoft Research
surajitc@microsoft.com
ABSTRACT
Data exploration is about efficiently extracting knowledge
from data even if we do not know exactly what we are look-
ing for. In this tutorial, we survey recent developments in
the emerging area of database systems tailored for data ex-
ploration. We discuss new ideas on how to store and access
data as well as new ideas on how to interact with a data sys-
tem to enable users and applications to quickly figure out
which data parts are of interest. In addition, we discuss how
to exploit lessons-learned from past research, the new chal-
lenges data exploration crafts, emerging applications and
future research directions.
1. INTRODUCTION
Assumptions in Traditional Systems. Traditional
data management systems assume that when users pose a
query a) they have good knowledge of the schema, meaning
and contents of the database and b) they are certain that
this particular query is the one they wanted to pose. In
short, we assume that users know what they are looking for.
In response, the system always tries to produce correct and
complete results.
Traditional DBMSs are designed for static scenarios with
numerous assumptions about the workload. For example,
state-of-the-art systems assume that there will be a tuning
phase where a database administrator tunes the system for
the expected workload. This assumes that we know the
workload, we know that it will be stable and we have enough
idle time and resources to devote to tuning.
Modern Exploration-driven Applications. The above
assumptions were valid for the static applications of the past
and they are still valid for numerous applications today.
However, as we create and collect increasing amount of data,
we are building more dynamic data-driven applications that
do not always have the same requirements that database
systems have tried to address during the past five decades.
Indeed, managing an employee or an inventory database is a
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http://dx.doi.org/10.1145/2723372.2731084
This work is partially supported by NSF grants IIS-1253196 and IIS-1452595.
drastically different setting than looking for interesting pat-
terns over a scientific database.
Consider an astronomer looking for interesting parts in a
continuous stream of data (possibly several TBs per day):
they do not know what they are looking for, they only wish
to find interesting patterns; they will know that something
is interesting only after they find it. In this setting, there
are no clear indications about how to tune a database sys-
tem or how the astronomer should formulate their queries.
Typically, an exploration session will include several queries
where the results of each query trigger the formulation of
the next one. This data exploration paradigm is the key
ingredient for a number of discovery-oriented applications,
e.g., in the medical domain, genomics and financial analysis.
Database Systems for Data Exploration. Such novel
requirements of modern exploration driven interfaces have
led to rethinking of database systems across the whole stack,
from storage to user interaction. Visualization tools for data
exploration (e.g., [38, 49, 66]) are receiving growing interest
while new exploration interfaces emerged (e.g., [18, 32, 45,
57]) aiming to facilitate the user’s interactions with the un-
derlying database. In parallel, numerous novel optimizations
have been proposed for offering interactive exploration times
(e.g., [6, 36, 37]) while the database architecture has been
re-examined to match the characteristics of the new explo-
ration workloads (e.g., [8, 27, 28, 39]). Together, these pieces
of work contribute towards providing data exploration capa-
bilities that enable users to extract knowledge out of data
with ease and efficiently.
Tutorial Outline. This tutorial gives a comprehensive
introduction to the topic of data exploration, discussing
state-of-the-art in the industry and in the academic world.
Specifically, it includes the following sections.
1. Introduction: We start with an introduction of the
concept of data exploration and an overview of the new chal-
lenges presented in the era of “Big Data” which make data
exploration a first class citizen for query processing tech-
niques. In this part, we also discuss the support available
in today’s products and services for data exploration tech-
niques and what is still missing.
2. User Interaction: We take an in-depth look the ad-
vanced visualization tools and alternative exploration inter-
faces for big data exploration tasks. We further divide this
last topic into three sub-categories: a) systems that assist
SQL query formulation, b) systems that automate the data
exploration process by identifying and presenting relevant
data items and c) novel query interfaces such as keyword
search queries over databases and gestural queries.
3. Middleware: The second part of the tutorial dis-
cusses research that aims at improving the performance of
data exploration by building various optimizations on top of
the database engine. This category includes research that
aims to offer interactive query latencies by exploiting data
pre-fetching and background execution of likely similar ex-
ploratory queries as well as research on approximate query
results.
4. Database Engine: The third part of our presentation
includes work that aims to rethink how the architecture of
database systems can be redesigned to aid data exploration
tasks. We will discuss work on adaptive storage layouts,
adaptive data loading and adaptive indexing as well as work
that aims to provide efficient support for interactive inter-
faces.
5. Future Work: Finally, we discuss forward looking
ideas on the topic of data exploration, discussing open prob-
lems and providing possible directions for future work.
2. FACETS OF DATA EXPLORATION
As explained above, an understanding of data exploration
has three key facets. Our presentation follows a top-down
approach, starting from user interaction layer, proceeding
with optimizations on top the database engine and con-
cluding with research that reexamines the architecture of
database management systems. Table 1 shows the cluster-
ing of the papers we discuss.
2.1 User Interaction
The tutorial will start by introducing research at the user
interface layer where the goal is to assist users (often not
database experts) to explore big data sets. Here, a large
body of research has recently focused on supporting ex-
ploratory tasks by providing alternative interfaces for in-
teracting with the underlying database.
Query Result Visualization. We first highlight the
role of visualization for data analytics [1, 38]. We intro-
duce visualization tools that assist users in navigating the
underlying data structures [61, 62]. We also cover tools that
incorporate new types of interactions such as collaborative
annotations and searches [48] as well as recommendations
of visualizations [40]. We then proceed to describe opti-
mization techniques that aim to support large-scale visual
analytics. These approaches include query result reduction
allowing for interactive visualization [11], rapid order pre-
serving sampling [12] and search for automatically identify-
ing interesting data visualizations [49]. We describe these
optimizations as well as vision work that proposes a novel
declarative visualization language [66] to bridge the gap be-
tween traditional database optimizations and visualization.
Exploration Interfaces. In this part, we discuss new
exploration interfaces that help users navigate the underly-
ing data space. We start with systems that automate the
data exploration process by discovering data objects [14, 18,
20] that are relevant to the user. These systems rely on user
modeling approaches, such as online classification based on
relevance-feedback [18] and models built by variations of
facet search techniques [20]. We compare these modeling
solutions and highlight their advantages and drawbacks.
We proceed with solutions aiming to assist users formulate
their exploratory queries [4, 67]. We cover systems designed
for users who are not aware of the exact query predicates
but who are often aware of data items relevant to their
exploration task or tuples that should be present in their
query output [64]. For example, in [13] the authors infer
join queries via labeling relevant objects, while in [58] and
[51] the focus is on discovering queries given example out-
put tuples. We also cover recent work on query learning
based on example tuples [3] as well as solutions for tuning
imprecise queries, where the relevance of query predicates is
uncertain to the user [52]. Finally, we cover techniques for
recommending SQL queries [21] as well as systems that iden-
tify the “best” segmentations of the data space to propose
to the user [57].
This part of the tutorial concludes with a discussion of a
series of novel query interfaces proposed recently. Specifi-
cally, we describe dbTouch [32, 44] and GestureDB [45, 47],
two user-guided visual tools for specifying relational queries
and for processing data directly in an exploratory way.
2.2 Middleware
The second part of the tutorial includes work that focuses
on improving the efficiency of data exploration tasks but
without changing the underlying data engine. All techniques
covered in this section are implemented in the middleware
between the user interaction layer and the database engine.
This brings flexibility in applying such ideas across several
systems, effectively improving the exploratory properties of
existing systems without requiring changes in the underlying
architecture.
Data Prefetching. Exploratory tasks can be compu-
tationally heavy, as users often execute long sequences of
unoptimized queries. The tutorial covers research that aims
to reduce the overall exploration time through result pre-
fetching techniques. Faster exploration times are achieved
by caching data sets which are likely to be used by a user’s
follow up exploratory query and the main challenge is iden-
tifying the data set with the highest utility.
Data prefetching has been studied within the context of
data exploration for a number of query types such as multi-
dimensional windows [36], data cubes [37, 55, 54] and spa-
tial queries [63]. In this part of the tutorial we discuss these
types of exploration queries and present alternative tech-
niques for identifying promising data sets for pre-fetching,
such as background execution of similar speculative queries [36,
37] as well as indexing and searching past users’ exploration
trajectories [63].
Finally, we discuss the interplay between result diversifi-
cation [41] and data caching. While returning diverse but
relevant data items can assist users in quickly navigating the
data space, it also adds significant computational overhead.
We discuss techniques that explore the trade-off between in-
troducing new results and re-using cached ones [41] as well
as optimization methods for diversifying query results [65].
Query Approximation. An alternative approach for
improving the response time of exploratory queries is to
present approximate results. We first discuss online process-
ing techniques [25] and the related CONTROL project [24].
These techniques offer approximate answers and their goal
is to allow users to get a quick sense of whether a particular
query reveals anything interesting about the data.
We then proceed with solutions that process queries on
sampled data sets to provide fast query response times. We
discuss the trade-off between results accuracy and query per-
formance and we present architectures that allow for query
execution over subsets of data. In such a setting, meeting
User Data Visual Optimizations Visualization Tools
Interaction Visualization [38] [11, 12, 49, 66] [40, 48, 61, 62]
Exploration Automatic Exploration Assisted Query Formulation Novel Query Interfaces
Interfaces [14] [18, 20] [3, 4, 13, 21, 52, 57, 58, 64, 51] [32, 44, 45, 47]
Middleware Interactive Performance Data Prefetching Query Approximation
Optimizations [36, 37, 41, 63] [16, 5, 6, 7, 24, 25]
Database Indexes Adaptive Indexing Time Series Flexible Engines
Layer [27, 39] [26, 29, 30, 31, 33, 22, 23, 50] [68] [17, 42, 43, 34]
Data Storage Adaptive Loading Adaptive Storage Sampling
[28, 8, 2, 15] [9, 19] [59, 60, 35]
Table 1: Clustering of current work on data management research for data exploration.
(Some papers may span multiple clusters; we discuss this in detail in the tutorial content but for simplicity we list
them under their primary area only in this table.)
user-defined error bounds is necessary to ensure reliable ex-
ploration results. The tutorial covers sampling techniques
for controlling the quality of the query results while bound-
ing their execution time [5, 6, 7, 59, 60].
2.3 Database Layer
The third research cluster includes work that aims at re-
thinking database architectures at their core [27]. Work in
this area reconsiders the fundamental methods to store and
access data to match exploration patterns. We organize this
work into four areas: adaptive indexing, adaptive loading,
adaptive storage and sampling based architectures.
Adaptive Indexing. In a data exploration scenario we
are searching for interesting data patterns without knowl-
edge of what we are looking for. Yet traditional database
systems rely heavily on tuning actions and accurate work-
load knowledge to achieve good performance. One of the
most critical tuning actions is that of choosing the proper
set of indexes. Making strict a priori choices means that
a system is not well prepared for an exploratory scenario
where users may focus on arbitrary data parts at differ-
ent times. Research on adaptive indexing introduces the
idea of creating indexes incrementally and adaptively dur-
ing query processing based on the columns, tables and value
ranges that queries request. Indexes are built gradually; as
more queries arrive indexes are continuously fine-tuned [26].
Adaptive indexing has been studied to improve selections in
column-stores [29], and has been shown to work in late ma-
terialization architectures [31], to allow for incremental and
partial projections [31], to be robust in workload changes
[23], to absorb updates efficiently and adaptively [30] and to
enable multi-query processing via concurrency control [22].
In addition, the basic algorithms have been studied in depth
in the face of trade-offs such as adaptation speed and ini-
tialization costs [33, 56] as well as optimized for modern
hardware [50, 10]. In addition, adaptive indexing has been
studied for supporting exploration in time-series processing
[68] and in Hadoop [53].
Adaptive Loading. During data exploration not all
data is needed. Adaptive loading exploits this fact and in-
troduces the notion that users can start querying a database
system (with efficient response times) even before all data
is loaded or even leaving some parts of the data unloaded,
effectively enabling efficient raw data access [28, 8, 2, 15].
Adaptive Storage. The way we store data defines the
best possible ways to access it. There is no perfect storage
layout; instead there is a perfect layout for each individual
data access pattern. Modern systems rely on static layouts
and build the whole architecture around a single layout. In
a data exploration scenario we cannot a priori decide what
is a good layout as we do not know the exact query patterns
up front, leading to sub-optimal performance for traditional
static systems. In this part of the tutorial, we discuss recent
work that aims at removing this problem through adaptive
storage [19, 9].
Flexible Architectures. Furthermore, there has been
a significant push towards flexible database architectures
where we can tune the architecture to the task at hand,
for example by having a declarative interface for the data
layouts [17] or for the whole engine [42, 43]. In another vi-
sion, organic databases are proposed to continuously match
incoming data and queries [34].
Architectures Tailored for Approximate Process-
ing. Finally, approximate processing is an important tool to
support data exploration. In addition to the ideas discussed
for sampling in previous sections, another line of work aims
to push sampling inside the core of the engine, creating an
architecture where storage and access patterns are tailored
to support sampling-based query processing efficiently [59,
60]. This allows for efficient access and updates of sampled
data sets. More recently, DICE combines sampling with
speculative execution in the same engine [35]. Approxima-
tion and sampling has also been coined as a key approach to
support interactive visual analytics at the core of a DB en-
gine [32]. Of course, approximate processing is a rich topic
in DB literature [16, 5]; Exploiting this knowledge to design
new engines is one of the open challenges.
2.4 Open Problems and Challenges
The final part of the tutorial presents open problems.
Some of these topics were introduced by recent vision pa-
pers [46, 39, 14]. The overall vision is to achieve data nav-
igation systems that automatically steer users towards in-
teresting data. Data system architectures should inherently
support exploration with storage and access patterns being
fully driven by the exploration paths taken by the users. A
system should be able to provide answers instantly even if
they are not complete, but it should also be able to even-
tually lead users towards interesting data patterns. Users
should be able to interact with the database at various lev-
els, ranging from manual steering using a declarative “steer-
ing” language to automatic steering with minimum query
formulation expectations from the user. Some of the ideas
include architectures that inherently support sampling at
the lower level (e.g., inside operators) as well as opportunis-
tically answering similar queries depending on where data
resides (e.g., data in L1 cache is cheap to show to the user
even if it is not exactly what the user requested). In addi-
tion, work on user profiles and interaction histories can play
a crucial role in optimizing exploration tasks.
Finally, we summarize the progress made towards the di-
rections introduced by these papers and highlight the po-
tential challenges both at the user interaction layer and the
database architecture layer. At the user interaction layer we
still lack declarative “exploration” languages to present and
reason about popular navigational idioms. Such languages
could facilitate custom optimizations, such as user-driven
prefetching, reusing past or in-progress query results and
customizing visualization tools. Other future directions in-
clude processing past user interaction histories to predict
exploration trajectories and identify interesting exploration
patterns. Similarly, at the database system layer there are
numerous opportunities to reconsider fundamental assump-
tions about data and storage patterns and how they can be
driven dynamically by high level requests.
Finally, we discuss the importance of interconnecting re-
search from both the user interaction and the database ar-
chitecture layers in order to provide a complete stack of
“exploration-ready” database systems.
3. BIOGRAPHIES
Stratos Idreos is an assistant professor of Computer
Science at Harvard University where he leads DASlab, the
Data Systems Laboratory@Harvard SEAS. Stratos works on
data systems architectures with emphasis on designing sys-
tems for big data exploration. For his doctoral work on
Database Cracking, Stratos won the 2011 ACM SIGMOD
Jim Gray Doctoral Dissertation award and the 2011 ERCIM
Cor Baayen award as <most promising European young
researcher in computer science and applied mathematics>
from the European Research Council on Informatics and
Mathematics. In 2010 he was awarded the IBM zEnterpise
System Recognition Award by IBM Research, and in 2011 he
won the VLDB Challenges and Visions best paper award. In
2015 he received an NSF CAREER award and was awarded
the 2015 IEEE TCDE Early Career Award from the IEEE
Technical Committee on Data Engineering.
Olga Papaemmanouil is an assistant professor of Com-
puter Science at Brandeis University since 2009. She re-
ceived her undergraduate degree from the University of Pa-
tras, Greece and completed her Ph.D at Brown Univer-
sity. Her research interests are in data management and
distributed systems with a recent focus on performance man-
agement for cloud databases and interactive data explo-
ration. She is the recipient of an NSF CAREER Award
(2013) and a Paris Kanellakis Fellow (2002).
Surajit Chaudhuri is a Distinguished Scientist at Mi-
crosoft Research and leads the Data Management, Explo-
ration and Mining group. In addition, as a Deputy Man-
aging Director of Microsoft Research Lab at Redmond, he
also has oversight of Distributed Systems, Networking, Se-
curity, Programming languages and Software Engineering
groups. He serves on the Senior Leadership Team of Mi-
crosoft’s Cloud and Enterprises division. His current areas
of interest are enterprise data analytics, data discovery, self-
manageability and cloud database services. Working with
his colleagues in Microsoft Research, he helped incorporate
the Index Tuning Wizard (and subsequently Database En-
gine Tuning Advisor) and data cleaning technology into Mi-
crosoft SQL Server. Surajit is an ACM Fellow, a recipient
of the ACM SIGMOD Edgar F. Codd Innovations Award,
ACM SIGMOD Contributions Award, a VLDB 10 year Best
Paper Award, and an IEEE Data Engineering Influential Pa-
per Award. Surajit received his Ph.D. from Stanford Uni-
versity in 1992.
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