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Progressive sequential pattern mining: steerable visual exploration of patterns with PPMT


Abstract and Figures

The progressive visual analytics (PVA) paradigm has been proposed to describe visual analytics systems whose main goal is to reach a thorough coupling between the analyst and her system by getting rid of waiting periods classically encountered during data processing. PVA systems use algorithms that both provide intermediate results throughout their execution, and are steerable by the analyst to change the strategy used to perform the remaining computation. Our focus is on progressive sequential pattern mining, as in the seminal work of Stolper et al. [30]. Here we go further mainly by considering the temporal nature of patterns related to their occurrences. We propose a pattern-oriented data model, a pattern analysis task model, and guidelines for designing progressive pattern mining algorithms. We introduce PPMT, a tool to support analysts in their progressive exploration of activity traces, based on a modification of the GSP algorithm. We evaluate our proposal on the technical performances of our progressive algorithm, and on the effect of steering on analysts’ performances.
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Progressive Sequential Pattern Mining:
Steerable Visual Exploration of Patterns with PPMT
Vincent Raveneau*
University of Nantes
Julien Blanchard
University of Nantes
Yannick Pri´
University of Nantes
Figure 1:
The main interface of PPMT:
(A) Information about the dataset, (B) history of the analyst’s actions, (G) information about
the state of the pattern mining algorithm and (H) discovered frequent patterns. Temporal visualization views present information
from the dataset and the selected patterns: (D) overview of the data, (E) events and pattern occurrences, (F) user sessions and
pattern occurrences. Analysts can select event types and users to highlight them in the temporal views. Selecting patterns adds
their occurrences to the views. (C) A summary of the selections is always available.
The progressive visual analytics (PVA) paradigm has been proposed
to describe visual analytics systems whose main goal is to reach a
thorough coupling between the analyst and her system by getting rid
of waiting periods classically encountered during data processing.
PVA systems use algorithms that both provide intermediate results
throughout their execution, and are steerable by the analyst to change
the strategy used to perform the remaining computation. Our focus
is on progressive sequential pattern mining, as in the seminal work
of Stolper et al. [30]. Here we go further mainly by considering
the temporal nature of patterns related to their occurrences. We
propose a pattern-oriented data model, a pattern analysis task model,
and guidelines for designing progressive pattern mining algorithms.
We introduce PPMT, a tool to support analysts in their progressive
exploration of activity traces, based on a modification of the GSP al-
gorithm. We evaluate our proposal on the technical performances of
our progressive algorithm, and on the effect of steering on analysts’
Index Terms:
Human-centered computing—Visualization—
Visualization systems and tools—Visual analytics; Information
systems—Information systems applications—Data mining
Even though the available computing power continuously increases,
classical data analysis systems still impose waiting times to ana-
lysts, due to the amount of data needed to be processed, and to the
complexity of the involved algorithms. Based on incremental visual-
ization, the progressive visual analytics (PVA) paradigm has been
proposed by Stolper et al. [30] to alleviate these difficulties through
the use of intermediate results and steerable algorithms. Several PVA
systems have been proposed for various data and algorithms [4,30].
In this paper, we focus on pattern mining techniques for PVA, and
more precisely sequential pattern mining, i.e. extracting patterns
from temporal data.
Sequential patterns can be viewed as sequences of events that
occur frequently in temporal data. Identifying and interpreting such
structures allows one to draw insights and knowledge about recur-
ring behaviors in the dataset, which corresponds to the last stages
of the data science pipeline (explore and communicate data) [11].
Sequential patterns were already used for PVA in the work of Stolper
et al., who proposed a pattern exploration tool to illustrate the PVA
paradigm. As their paper was focused on formulating a general
definition of PVA, independently of the algorithmic techniques in
use, they did not take into account the temporal nature of sequential
patterns, only considering them as objects described by a name and
some numerical statistical attributes. However, an analyst exploring
data with sequential patterns is involved in a temporal data explo-
ration activity, which implies tasks such as localizing occurrences
in time, checking their duration, frequency or periodicity, and many
more [1, 18].
In this paper we study the design of a PVA system for exploring
temporal data using sequential pattern mining algorithms, taking
into account these time-related considerations. We first investigate
the actions an analyst working with sequential patterns may want to
perform, based on an existing temporal data analysis task model [1].
We then propose some requirements on how to design a progressive
pattern mining algorithm. We also present a working progressive
version of the classical GSP pattern mining algorithm. We describe
PPMT, our PVA tool for activity data analytics which integrates our
modified version of GSP. Eventually, we report on the results of two
studies: one about the performances of the progressive algorithm,
and the other about evaluating analysts’ steering interactions with
the algorithm within PPMT.
In summary, our contributions are the following:
A data and task model for an analyst exploring temporal data
with patterns, instantiated from the general model of Andrienko
and Andrienko [1].
Five guidelines for designing a progressive algorithm for se-
quential pattern mining.
A pattern-based data exploration tool named PPMT, that im-
plements a progressive sequential pattern mining algorithm
featuring two new kinds of steering (towards sequence and
An evaluation of our contribution both on the technical side
of our progressive algorithm, and on the effect of steering
on analysts performances, pointing towards the relatively low
impact of progressiveness on execution time, and the increased
rapidity of analysis for question answering.
2.1 Mining sequential patterns
Sequential patterns are extracted from sequential data, i.e. a col-
lection of symbolic event sequences. An event has at least two
properties: a type that describes the event characteristics, and a
timestamp stating when the event occurred. In its simplest form, a
sequential pattern consists of an ordered list of event types (written
ABC and the like in this paper). It has two properties: a size, the
number of these event types, and a support, the number of occur-
rences of the pattern in data. Pattern mining algorithms only extract
patterns that have a support at least equal to a user-given threshold,
called frequent patterns. Other user-given parameters are used to
constrain the search for patterns, such as their occurrences maximum
durations. Since we focus on this kind of patterns, the terms pattern
and sequential pattern will be used with the meaning of frequent
sequential pattern in this paper.
Two kinds of sequential patterns are present in the literature,
differing in the data structure analyzed and the way patterns’ occur-
rences are counted, as illustrated in figure 2. Sequential patterns,
proposed by Srikant and Agrawal [29], are extracted from a set of
sequences. Counting the occurrences of a sequential pattern consists
in counting the number of sequences in which it is found, regardless
of how many times it is present in each sequence. Episodes, pro-
posed by Mannila et al. [20], are extracted from a single sequence.
Counting the occurrences of an episode consists in counting the
number of times it appears in the sequence from beginning to end.
Many pattern mining algorithms for sequential data have been
proposed, that can be classified in two categories. Apriori-like
algorithms, such as GSP [29], PSP [22], SPADE [33], SPAM [3],
Figure 2: Occurrences of BC as sequential pattern (left) and episodes
(right). The occurrences are coloured in red. The sequential pattern
BC has 2 occurrences in the left dataset, while the episode BC has 3
occurrences in the right dataset.
LAPIN [32], WINEPI [21] and MINEPI
[19] generate candidate
patterns and check them by scanning data to determine if they are
frequent or not. The candidates are generated by extending shorter
frequent patterns with frequent event types. These algorithms can
either do a breadth-first search for frequent patterns, where they will
generate and check all the candidates of a given size before moving
on to longer ones, or they can do a depth-first search. In this case,
they will try to extend a given prefix as much as possible, before
moving on to another one.
FP-Growth algorithms, such as FreeSpan [13], PrefixSpan [26]
and SLPMiner [28] build a tree-like compressed representation of
the data called the FP-tree. If it fits into memory, this representation
is then used to extract the frequent patterns faster than with Apriori-
like approaches. The construction of the FP-tree is however time-
2.2 Interactive and visual pattern mining
Goethals et al. [10] designed a system for interactive visual pattern
mining. They did not propose a steerable algorithm, but their system
was interactive enough to allow the user to alternate mining sessions
and pattern exploration sessions. Their approach heavily relied
on statistical interestingness measures for rules and itemsets. Van
Leeuwen stressed the need to integrate the analyst into the pattern
mining pipeline [31]. He insisted on the importance of exploiting
user feedback and learning subjective interestingness, and suggested
to use pattern set mining techniques instead of classical patterns.
Other researchers worked on how to improve post-analysis of
algorithms results, mainly through visualizations. Among the pro-
posed solutions one can find graphs [6, 9], matrices [12, 15], or
scatter plots [16]. However, only a few works deal with pattern
representations, such as Liu et al. [17]. The number of work tackling
pattern occurrence representations is also small, and usually in very
specific cases, such as medical data with Monroe et al. [23] or Kwon
et al. [14].
Note that all these works concern classical patterns but not tem-
poral patterns.
2.3 Progressive Visual Analytics
Building on incremental visualization works [2, 7], Stolper et al.
have proposed Progressive Visual Analytics (PVA) as a paradigm
to describe visual analytics systems whose main goal is to reach a
thorough coupling between the analyst [30]. PVA revolve around
three core aspects, (1) designing analytic algorithms able to produce
meaningful partial results during their execution, (2) being able to
steer the remaining execution of these algorithms, and (3) having vi-
sual interfaces allowing to manage the partial results without altering
the analyst’s cognitive workflow. [30] provides some guidelines on
WINEPI and MINEPI extract episodes, while the others extract sequen-
tial patterns
how to design such systems, that have been enhanced and completed
by other works [4, 25, 34].
A few progressive systems have been presented in the literature,
such as Stolper et al.’s ProgressiveInsights [30] (described in the
next paragraph) and Badam et al.’s InsightsFeed [4]. InsightsFeed
was designed to allow an analyst to perform progressive analytics
over Twitter data. It creates chunks of tweets that are processed
when they reach a certain size. Several computations are performed,
like sentiment classification and keyword extraction, and a map
of these information is constructed using clustering (k-means) and
dimensionality reduction (t-SNE projection). Users can control
the execution of the algorithms by pausing them or going back
to previous steps. The remaining computation can be steered by
changing data chunk size, update speed, or clustering and projection
parameters, the new value being taken into account for the next
computation step.
To our knowledge, only Stolper et al. have used patterns in
PVA [30]. Their system ProgressiveInsights allows an analyst to
explore the outputs of a progressive version of Ayres et al.’s SPAM
pattern mining algorithm [3]. Progressively extracted patterns are
presented with a scatterplot and in lists ordered by the patterns’
properties. This limits the exploration and interpretation to these
properties, the analyst being unable to get access to individual
occurrences. Computation can be steered by prioritizing the search
for patterns having a given prefix, or by ignoring the potential
extensions of given prefixes.
As a conclusion, several ways of adding interactivity to an al-
gorithm’s execution have been suggested. The interactive mining
community proposed techniques where the execution runs, stops,
then restarts or continues after an action from the analyst, tending
to favor confirmatory analysis at the cost of potential unexpected
discoveries. In PVA’s approach the algorithm is less relying on the
analyst for its execution, while still offering the possibility to steer
the computation at will. The only work involving patterns is an
illustration of the definition of PVA [30]. However its handling of
patterns does not fully take into account the temporal nature of their
occurrences. In the remainder of this paper we present our work
towards a better integration of such particularity in PVA.
In this section we present our vision for progressive pattern mining.
Patterns are useful to understand the data they were discovered in, so
we first investigate the tasks one may want to perform with patterns.
We then reflect on this list, to determine how some of its elements
can be part of a progressive data analysis workflow. This leads
us to identify guidelines for designing progressive pattern mining
3.1 Sequential pattern analysis tasks
Several models of the tasks an analyst can perform while working
on data have been proposed [1, 5]. Andrienko and Andrienko [1]
proposed a general model for high level tasks, that we instantiate
considering an analyst exploring sequential patterns discovered in
data. The reason we chose this specific model is that it has been
designed to focus on analysis tasks one may specifically perform
on temporal data. Moreover, the model is formal and exhaustive,
with respect to the data representation used. We present the original
general model from [1], along with our pattern-oriented model.
3.1.1 Data representation
General model:
A dataset is made of two types of variables, the
referrers and the attributes. The referrers are the dimensions of
the data, while the attributes store the values which appear at the
intersection of the referrers. For each combination of values of
the referrers, there is at most one combination of values for the
attributes. Thus, the data representation can be seen as a function
R1×R2×. . . Rp
A1×A2×. . . Aq
is the value
domain of the
th referrer and
is the value domain of the
attribute. For example, in a demographic dataset, space and time are
the referrers, respectively discretized by administrative districts and
years. The attributes are the variables measured each year in each
district, such as number of persons, number of unemployed persons,
employment rate.
Pattern-oriented model:
We identify three referrers and one at-
tribute for temporal data exploration with sequential patterns:
referrers: sequence,time and pattern.
attribute: occurrence
This corresponds to the basic purpose of an episode mining algo-
rithm, which for any pattern searches for all its occurrences in any
sequence at any time. The values of the variables sequence,pattern
and occurrence can be seen as objects, referenced by an ID and
described by properties allowing to designate sets of objects without
indicating their IDs. For example:
the sequences may be described by a category or their length
(total duration);
the patterns may be described by their syntax, their size (num-
ber of event types), or an interest tag given by the analyst;
the occurrences may be described by their duration.
The time referrer is the usual time scale that can be continuous or
discrete, with the many associated properties (day or night, weekend
or not, name of the month, etc). While in our work we consider
the values for this referrer to be points, we don’t see any important
change when using ranges.
More precisely, in mathematical terms, the data representation
for temporal datasets analyzed with patterns is this function d:
(p,s,t)7→ oif ois an occurrence of pin sat time t
is the timescale,
is the set of data sequences,
is the set
of all the patterns generated by the mining algorithm, and
is the
set of all the occurrences identified by the mining algorithm. An
example of how to use this model is given in figure 3, where three
occurrences of a pattern BC are described with the dfunction.
Note that the representation proposed in this section encompasses
not only the patterns but the whole dataset since data events can be
seen as patterns of size 1. So the representation can be used both
to answer questions about the dataset and the patterns that can be
found inside.
Figure 3: Example of a small sequence dataset (left). On the right,
some values for the data function dare given.
3.1.2 Tasks model
Andrienko and Andrienko provide an exhaustive task classification
in 11 categories in their general model. All these categories also
exist in our pattern-oriented model since our data representation
is directly specialized from their general data model. Due to the
lack of space, we do not present the 11 categories in details. We
rather provide illustrative examples related to patterns for the 6 most
high-level tasks, which Andrienko and Andrienko consider to be the
most performed during exploratory data analysis.
Synoptic direct lookup:
Are the occurrences of pattern ABC uni-
formly distributed over the sequences?
Synoptic inverse lookup:
In sequence 15, during which time inter-
vals does the pattern ABC occur regularly each 3 days ?
Synoptic direct comparison:
Compare the temporal distributions
of the occurrences of pattern ABC between sequences 1and 2.
Synoptic inverse comparison:
For the sequences of the control
group, compare the periods in which ABC collapses with the periods
in which ADE rises.
Synoptic relation-seeking:
In which sequences is the temporal
distribution of ABC occurrences identical to the one in sequence 5?
Connectional task:
Is the activity in sequence 45 influencing the
activity in sequence 71?
In theory, the 11 task categories are valid, and a system for ex-
ploring temporal data with sequential patterns should support them
all. However, this has to be adjusted, taking into account a strong
dichotomy among the three referrers. The time values form a met-
ric space, and the sequence values form a population, i.e. a set of
elements that can reasonably be considered as independent (this
is the usual statistical assumption). But both time and sequence
allow to consider a distribution over them. For example, one may be
interested in the distribution of the number of occurrences of pattern
ABC over time, or over the set of sequences (this last distribution is
made more readable by ordering the sequences). On the contrary, it
is not appropriate to consider a distribution over the pattern referrer
since patterns are clearly not independent. Larger patterns are built
from the shorter ones, and some inclusion relations stand among
them (e.g. AB is included into ABC), which introduces a bias into
the distribution. This makes a big difference for the synoptic tasks,
which consider a set of attribute values in its entirety by means of
distributions (what Andrienko and Andrienko call a ”behaviour”,
including temporal trends and spatial repartitions). We claim that
the synoptic tasks
involving a distribution over patterns
are not
rigorously correct in the general case, and may be misleading in
practice, especially if the pattern population is not narrowly selected
to avoid relations among patterns. Even though this affects the 6
synoptic task categories, all of them remain valid when consider-
ing tasks involving a distribution over the other referrers. It will
have consequences on the design of our system in section 4, by
prioritizing time and sequences to structure the visualizations.
3.2 Guidelines for progressive pattern mining algo-
We present guidelines for designing progressive algorithms for se-
quential pattern mining. The guidelines G1, G2 and G4 directly
result from the pattern-oriented model we just proposed.
G1: Extract episodes.
As shown in the pattern-oriented model, the
notion of occurrence is central when exploring temporal data with
patterns. However, most of sequential pattern mining approaches
belong to the tradition of Srikant and Agrawal works [24], which
are looking for the sequences in which a pattern is found, rather than
where the pattern is found in these sequences. That is why episode
mining algorithms (cf. Mannila et al. [20] works) seem more ap-
propriate for the exploration of a temporal dataset, as it allows to
explore and analyze the context in which patterns occurrences are
discovered. As episode mining deals with only one sequence, one
needs to: either adapt an episode mining algorithm to read several se-
quences (by summing the occurrences over the sequences); or adapt
a sequential pattern mining algorithm to extract all the occurrences
(by scanning sequences from beginning to end). The first solution
is easier, but most of open source pattern mining algorithms mine
sequential patterns.
G2: Save occurrences.
The result of a pattern mining algorithm
is a set of frequent patterns with numbers of occurrences. More
precisely, the pattern occurrences are identified and counted but not
saved. For exploring temporal data by means of patterns, and in
accordance with our pattern-oriented model, one needs to save the
occurrences. This is a small change in the algorithm, but it largely
increases the memory needs.
G3: Use a BFS Apriori-like strategy.
Being able to provide inter-
mediate results during the computation is mandatory for a progres-
sive algorithm. With regards to pattern mining, this makes Apriori-
like strategies better candidates to modification than FP-growth’s.
Indeed, Apriori-like algorithms start checking candidates and gen-
erating patterns right from the start of their execution, whereas
FP-growth algorithms need to build the FP-tree before beginning
generating any pattern. Furthermore, Stolper et al. [30] identified
that breadth-first search strategies are preferable over depth-first ones
for progressive pattern mining: since short patterns are the building
blocks of the longer ones, focusing the early stages of the compu-
tation on the extraction of short patterns gives a better overview of
what can be expected later.
G4: Propose steering on patterns, sequences and time.
We saw
in section 3.1.1 that referrers are the access keys to the attributes.
This justifies that algorithm steering should be done by letting the
analyst focus on referrer values. Following the pattern-oriented task
model we proposed, this means that the algorithm must be able to
prioritize the occurrence search on some patterns, some sequences,
or some time periods.
G5: Make the analyst aware of the algorithm activity.
the analyst needs to interact with the algorithm, it is important
that he had information at any time about what the algorithm has
already done, or is currently doing. In the case of a pattern mining
algorithm, important information are the current explored pattern
size, the number of candidates of this size (both already checked and
remaining), and whether steering is occurring or not (and its target).
Other information might be of use, such as the speed at which the
algorithm is performing its search for frequent patterns.
PPMT is a tool designed to assist an analyst’s exploration of activity
traces. Its interface (figure 1), allows the analyst to explore the
data and interact with a progressive serial episode mining algorithm
running in the background. In this section we present PPMT’s
architecture, our progressive pattern mining algorithm, and PPMT’s
widgets related to the progressiveness of the analysis.
As activity traces originate from users interacting with a system,
in this section the sequence referrer is called user, since its values
are the user IDs in the data.
4.1 General architecture
PPMT is based on the architecture we proposed for progressive
pattern mining algorithms [27]. In this implementation, we chose a
client-server architecture, where the server is in charge of providing
the data and running the pattern mining algorithm, whereas the client
constructs the visualizations presented to the analyst and manages
the interaction. The dataset is stored by both sides, as well as the
names of discovered patterns. The server has the detailed list of
pattern occurrences, the client storing only the ones that have been
explicitly requested. Communication between the two sides is done
through websockets, allowing both the client and the server to initiate
a communication when needed.
When the analyst accesses the client, a list of available datasets
is displayed. Selecting one starts the transmission of its content
from the server, and establishes the websocket connection that will
be used for the rest of the session. When the client has received
all the data, the server starts running the pattern mining algorithm.
From this moment on, communications from the client to the server
are instructions to steer the algorithm, or requests for the detailed
occurrences of specific patterns. In addition to answering these
queries, communications from the server to the client consist in
transmitting information about the newly extracted patterns and
about the algorithm’s state and progression.
4.2 Our progressive pattern mining algorithm
Our main scientific topic being interaction in PVA, we did not de-
velop a progressive pattern mining algorithm from scratch, but in-
stead chose to adapt an existing open source implementation as
in [30]. To comply with guideline G1, we wanted to extract episode
rather than sequential patterns, but no implementation of this kind
of pattern mining algorithm was available at that time (in 2017;
let us note that since June 2018 some are proposed in the SPMF
library [8]). Instead, we selected a sequential pattern mining algo-
rithm and modified it to extract episodes, by changing how pattern
occurrences are counted (see 4.2.2). Other changes were made to
turn the algorithm into a progressive one (see 4.2.3 and 4.2.4).
Since progressive algorithms need to communicate with the ana-
lyst’s tool, we integrated a dedicated bidirectional software interface
into the algorithm. On the one side this interface stores the incoming
steering requests into a queue that is regularly checked during the
execution. On the other side it is used by the algorithm to notify the
client or other plugged in parts of the code about its current state or
a newly discovered pattern.
4.2.1 Choosing a mining strategy (from guideline G3)
To implement our progressive pattern mining algorithm, we chose
to start from the GSP [29] algorithm, available in the SPMF library
[8]. GSP is well-known (many pattern mining algorithms were
actually designed as its variations) and it natively uses a breadth-first
search. As a breadth-first Apriori-like sequential pattern mining
algorithm, GSP computes the frequent event types in the data, then
combines them to generate all the candidates of size 2. Each of these
candidates is then checked to see if it is frequent or not. Infrequent
candidates are discarded, and frequent ones are combined to obtain
the candidates of size 3. This process continues until a user-given
size limit, or when no candidates or frequent patterns are found.
4.2.2 Extracting episodes and occurrences (from G1 & G2)
We modified GSP’s behavior to extract serial episodes instead of
sequential patterns, by keeping track of all the pattern occurrences
rather than the sequence ID they were discovered in. This change
also led to use an absolute support threshold instead of a relative
one to determine if a candidate is frequent or not. In addition to the
support threshold, our algorithm takes the following parameters to
provide boundaries for the pattern space:
The gap (minimum and maximum) controles the number of
events allowed to be found between events forming a pattern
occurrence, while not being part of the pattern. This can be
interpreted as a control over the level of noise allowed in the
pattern occurrences.
The maximum size, expressed as a number of event types,
preventing the algorithm to search for patterns too long to
carry any important meaning.
The maximum duration of a pattern occurrence, expressed
in milliseconds. This effectively acts as the time window
constraint that is often found in episode mining.
4.2.3 Providing intermediate results (from guideline G5)
GSP stores the frequent patterns and presents them all at once at the
end of its execution. We output a frequent pattern as soon as all its
occurrences are discovered so as to be able to produce intermediate
results for exploration. Since intermediate results are an important
part of PVA, we could have had the algorithm output a pattern as
soon as enough occurrences have been found to make it frequent,
and then update the number of its occurrences when new ones are
encountered. However, we decided to wait for the completion of the
occurrence search process before outputting a frequent pattern. This
choice was made to reduce the amount of information to process
and update, and to increase the user’s confidence in the numbers
displayed: users know they will not change anymore. Also, con-
sidering the speed at which a single pattern is processed, it would
most of the time be a very small gain, probably unnoticeable to the
end user. While searching for patterns, the algorithm also regularly
outputs the number of candidates that have been verified, in order to
communicate about its progression.
4.2.4 Steering the algorithm (from guideline G4)
We implement steering using the previously mentioned software
interface. Before checking a new candidate, a verification is made to
see if a steering has been requested since the previous check. If it is
the case, the current state of the pattern search (the current size and
the list of unverified candidates) is saved, and the candidate verifica-
tion continues depending on the required steering, as described in
the next paragraphs. During the steering, since our knowledge about
frequent patterns of a given size is incomplete, candidates of size
are no longer obtained by combining frequent patterns of size
Instead, candidates of size
are combined with the frequent
event types to generate the size
candidates. When the steering has
completed, the previously saved state of the pattern search is loaded,
information about the frequent patterns found during the steering is
added to it, and the default strategy resumes from this point.
Steering on pattern syntax:
Every candidate is compared to the
syntax template, and the search for occurrences only starts if the
comparison matches.
Steering on a time period:
The search for a candidate’s occur-
rences takes place over this period. If at least one occurrence of the
candidate is found in this period, then the search is continued over
the whole dataset for this candidate. Otherwise, the candidate is
rejected for the current steering and the algorithm moves on to the
next one.
Steering on a user:
The search for a candidate’s occurrences takes
place over this user’s trace. If at least one occurrence of the candidate
is found in this trace, then the search is continued over the whole
dataset for this candidate. Otherwise, the candidate is rejected for
the current steering and the algorithm moves on to the next one.
Steering on syntax is the easiest type of steering, because a syntax
constraint allows to directly filter out candidates without reading the
dataset. On the contrary, even in the case of a candidate that does
not satisfy the steering constraint, steering on time or user requires
reading parts of the data. However, extracting all occurrences instead
of just those present in the targeted period or user trace has the benefit
of keeping a constant strategy to determine if a candidate is frequent.
The same support threshold can be used during both the steering
and non-steering phases, and all the frequent patterns discovered
during a steering phase remain valid for the future. While it might
be possible to devise other ways of doing it, we did not found any
satisfying enough. For example, using a fraction of the support
threshold (i.e. using 50% of the usual value if the steering targets
half the dataset’s users) could only work if the pattern occurrences
were uniformly distributed over the data.
4.2.5 Comparison with Stolper et al. [30]
In their original versions, the algorithm we use (GSP) and the one
used by Stolper et al. (SPAM) differ in the order in which they
discover frequent patterns. As said previously, GSP is a breadth-first
algorithm while SPAM uses a depth-first strategy, but the set of
patterns obtained at the end is the same. In their implementation,
Stolper et al. modified SPAM to make it use a breadth-first strategy.
Both our implementation and theirs also add the possibility to steer
the computation towards patterns with a given prefix.
Our progressive algorithm goes further on two points. First, we
offer more steering options, namely steering on a time-period and on
specific users. Secondly, our algorithm allows the analyst to explore
patterns’ behaviour in time, by means of the occurrences.
4.3 PPMT’s interface
As illustrated in figure 1, PPMT’s graphical interface presents many
information to the analyst. On the left, the analyst is presented with
(A) the selected dataset, event types and users; and (B) an history
of her interactions with the system. On the right, (G) the current
state of the pattern mining algorithm is displayed, along with (H)
the discovered frequent patterns. Items from the lists of event types,
users and patterns can be selected with a click, and (C) a summary of
these selections is available. Three coordinated temporal views are
available to explore the dataset and the patterns. The first is (D) an
overview of the dataset, displaying an aggregated visualization of the
events. A brush tool allows the analyst to select a subset of the whole
dataset, that will be displayed in the other two views. The second
represents (E) the dataset’s events in the selected subset, as well as
occurrences of patterns selected by the analyst. Depending on the
selected time span, events are aggregated or displayed individually
(see figure 6). Finally, the third view displays (F) the user sessions,
where each rectangle represents a period where the user was active.
In this subsection, rather than presenting the whole interface, we will
focus on the elements related to the patterns and to the progressive
nature of the pattern mining algorithm.
4.3.1 State of the algorithm
Figure 4: Detailed information about the pattern mining algorithm
presented to the analyst.
Information about the state of the pattern mining algorithm is
available to the analyst as shown in figure 4. The strategy is either
”default strategy” or an indication of the current steering target. Two
graphs are displaying the speed at which the algorithm has been
checking candidates and discovering frequent patterns over the last
minute. The table displays detailed information about the extraction
of every pattern size, such as the number of candidates needing to
be verified and the elapsed time. A color code is used to represent
completed pattern sizes (green), the current target of the algorithm
(blue) and incomplete pattern sizes (orange). This detailed view is
not always displayed but a condensed version is always visible, in
the top right corner of PPMT’s main interface (see figure 1, (G)).
The analyst can open the detailed view by clicking on the condensed
4.3.2 Steering the algorithm
The analyst can request a steering of the algorithm by using three
different buttons. The button to steer on a time period is located
above the overview (D in figure 1), where the corresponding time
period is selected with the brush. Hovering over an item in the
pattern list makes a button appear on the line to request a steering
using the corresponding pattern as prefix. Steering on a specific user
is done in the same way, with a temporary button appearing while
hovering over the user list.
4.3.3 Control over the patterns
The discovered frequent patterns are displayed in a list, as shown in
the lower right part of figure 1. The analyst has some control over
this list, that are shown in more details in figure 5. The support and
size sliders and the name (i.e. syntax) text field are filters that can be
expected from any pattern analysis tool, but the other elements are
tied to the progressive nature of PPMT. Since patterns are discovered
while the analyst uses the system, adding entries to the pattern list
while the analyst interacts with it is problematic. To prevent this,
the analyst has the possibility to toggle on or off the live update
of the list. When active, any new pattern is added to the list when
received. When the option is toggled off, new patterns await to be
integrated into the list, and the analyst can see how many of them
are available. The list is created from all the patterns, by default and
before applying any filter. However, since a steering phase targets
patterns the analyst is interested into, it is possible to create the list
only from the patterns that have been found during the last steering
Figure 5: Controls over the discovered patterns available to the ana-
lyst. There are filters over the list, along with options to change the
way it is constructed and updated. The numbers at the top reflect
(from right to left) the total number of discovered patterns, the number
of patterns used to create the list (equal to the total unless the live
update is off), and the number of patterns remaining after applying
the filters.
4.3.4 Patterns Visualization
Patterns are visualized in the central timeline, as illustrated in figure
6. When a pattern is selected, an overview of its occurrences’ tem-
poral positions is displayed under the timeline, each black dot being
an occurrence. If the analyst wants to focus on a specific occurrence,
zooming in on it displays a detailed view of the individual events
composing the occurrence by linking them together. This allows the
analyst to observe the context of the occurrence in term of preceding
and following events, which can be useful to her understanding of
the data at hand. Patterns are also visible in the user session view,
in the bottom central part of figure 1; sessions where a selected
pattern is present are displayed in red, while blue sessions indicate
its absence.
In this section, we present the two evaluations we conducted on
our progressive algorithm and on PPMT’s steering features. Both
evaluations were conducted with the same dataset, collected while
students were using COCoNotes
, an online video consumption and
annotation platform. The data contains 201 000 events, 32 event
types and 211 users, over a four months period from September
2016 to January 2017.
Figure 6: Visualizations of a pattern’s occurrences. Depending on
the zoom level, the analyst can either see an overview of where the
occurrences are (black dots on the top image) or a detailed view of
the events composing the occurrences (linked events on the bottom
5.1 Effect of progressiveness & steering on the algo-
Having a progressive version of an existing algorithm may lose its
interest if the original one is much more efficient, so we wanted
to see how our progressive algorithm performed compared to the
original GSP. The main uncertainty lies in the impact of steering on
computation, so we decided to compare the following configurations:
(1) the original GSP algorithm modified to extract episodes, (2) our
progressive GSP, (3) our progressive GSP performing pattern syntax
steering and (4) our progressive GSP performing time steering. We
chose not to test user steering, because of its mechanical similarity
with time steering, as explained in section 4.2.4. In configurations
(3) and (4), steering on a random element (pattern or time-period)
was executed once for each pattern size, which amounts to about
15 steering phases per execution. An execution lasting between 3
and 20 minutes, we believe that –though not a perfect recreation of
a real use case– it is sufficient to highlight any impact the steering
could have on the algorithm, and to provide relevant insights about
the various configurations’ performances.
We measured the CPU time and maximum memory usage needed
to extract all the patterns in the dataset. Regarding the algorithm’s
parameters, we used constant values for the gap (min=0, max=2),
maximum size (20) and maximum duration (30s), but used three
different support thresholds, to obtain the following scenarios:
Scenario 1:
Min. support of 150, leading to 3492 frequent patterns
Scenario 2:
Min. support of 50, leading to 21731 frequent patterns
Scenario 3:
Min. support of 30, leading to 49121 frequent patterns
Each configuration was run 20 times over each scenario, on a
single dedicated CPU core, and we computed the average values for
CPU execution time (figure 7) and memory usage.
Figure 7: Results of the comparison between the different algorithms,
regarding the CPU time necessary to discover every pattern.
As can be seen in figure 7, the relation between the different
algorithms for CPU usage is the same for all the scenarios, with the
original GSP being faster than the progressive one. While perform-
ing similarly when extracting a few patterns, relaxing the parameters
to obtain more patterns increases the difference in computation time
between each configuration. We can also see that steering the algo-
rithm on pattern syntax has less repercussions on the performances
than steering on a time period. This was expected, since steering on
time requires additional data reads compared to steering on syntax,
as explained in section 4.2.4.
As far as memory usage is concerned, our first results show that
GSP uses more memory than Progressive GSP with or without
steering, which seems logical as GSP has to maintain the full list
of all occurrences until the end of its execution. Our system also
uses memory outside of GSP to store those occurrences, so memory
usage of progressive algorithms needs further investigation.
5.2 Effect of steering on analysis tasks
5.2.1 Protocol
We focus on steering, the main interaction between the analyst and
the progressive algorithm. We recruited 14 participants within our
laboratory (13 males, 1 female, mean age 26.1
7.05), all with
some experience with data analysis. After participants signed a
consent form, they were given some reminders about pattern mining
and a presentation of PPMT, as well as a list of analysis questions
they had to answer with PPMT. Only 7 participants had a PPMT
version with which they could steer the algorithm (steering group).
The questions were the following:
How many occurrences of the pattern
paused played Visibili-
tyChange are present in the data?
Q2 How many users have the pattern paused created played?
How many patterns are discovered for the user user049, in his
session of October 5th, from 9:02am to 9:11am?
Give two patterns of a size strictly greater than 2, present both
in the first session of user user153 AND in the first session of
user user177 (September 18th, from 9:38am to 11:15am).
We are interested in the events
that can follow or precede the
M = Mdp media play played
. How many patterns of
the form x M or M x are found?
Questions 1, 2 and 5 were designed to encourage the use of
steering on a pattern syntax, while questions 3 and 4 were more
suited to steering on a time-period. The questions had to be answered
in this order, and pattern mining was restarted for each question.
We did not make use of user steering, because the whole evaluation
session was already long enough (60 to 75 min), with participants
needing to assimilate a lot of information about PPMT’s features
and the dataset. For each question and each participant we collected
the duration, and the answer, graded as correct or incorrect (false or
non answered).
After using PPMT participants had to give their agreement with
a 5-points Likert scale on 14 affirmations on their global feedback,
pattern progressive extraction, and steering (only for the steering
group). Then they could freely comment on their experience.
5.2.2 Results
The time participants spent on each task is presented in figure 8.
We can see that participants from the steering group were faster for
every question, except the first one. This was expected, since the
first question was very easy to answer even without steering. We
computed Wilcoxon Signed-rank test for questions 2–5 and for the
whole set of questions. The non-significant results show an effect of
the steering/non-steering condition on the response times with the
following p-values: Q2: 5.3%, Q3: 12.8%, Q4: 5.3%, Q5: 14.1%,
Q1–5: 7.2%.
Regarding the correctness of the answers, steering group partic-
ipants gave 26 right answers, 8 wrong ones and did not answer 1
question. The other group gave 20 good answers, 12 wrong ones
and did not answer 3 questions. On an individual basis, most of the
14 participants gave the correct answers for questions 1, 2 and 4.
Only 7 correctly answered question 5, mostly because they did not
answer for both syntax xM and Mx. Only 5 gave the correct answer
to question 3 (see discussion).
The steering group participants used steering for almost every
question. They were able to target the information needed to answer
the questions in one unique steering request, but one participant did
multiple steering on more and more precise time periods to answer
question 3. The most notable exception to the use of steering was
for question 5, where 3 out of 7 decided not to use it since it would
only give them half of the answer. For questions 3 and 4, only 2
members of the steering group did not use time steering.
Figure 8: Summary of time needed for participants to perform the
tasks. Graduations are in seconds.
Participants’ feedback is presented in figure 9. The steering group
participants were unanimously agreeing with its usefulness for the
analysis, and the fact that it allows them be be faster when they have
a goal, while feeling in control of the computation.
5.2.3 Discussion
With regards to accuracy, the steering group performed slightly
better, however more participants would be needed to conclude
about the potential positive impact of steering on the correctness of
With regards to speed, the results are encouraging as the steering
group participants were globally faster than the others to answer
questions. This can be associated with their feedback that steering
was augmenting their confidence, helping them save time and feel
in control, while being generally useful. For example, one of the
participants declared that ”having the ability to steer the algorithm
really makes the analysis more fluid”, while another said that ”with-
out being able to steer the computation, [he] would have probably
Figure 9: Feedback from the participants after performing the tasks
given up on some questions”. This also echoes some remarks for-
mulated by two of the participants of the non steering group, that
had the feeling they would have wanted a way to ask for specific
patterns. While the difference between steering and non-steering
groups favors the steering by one or two minutes, it is important
to keep in mind that we designed the questions so that both groups
were able to answer them in a reasonable time. Had we chosen
different questions (essentially involving longer patterns), the gap
between the two groups could have been significantly larger.
An interesting observation originated from the way members
of the steering group dealt with question 3. Being able to steer
the computation allowed them to greatly shorten the time needed
to answer, however as soon as results stopped coming in during
the steering, 2 of them gave their answer without waiting for the
completion of the steering.
We presented some guidelines for designing progressive pattern
mining algorithms based on a data and task model inspired by [1].
We then introduced PPMT, our tools for progressive pattern analysis
of usage traces. Our evaluation points towards both the relatively
low impact of progressiveness on execution time, and the increased
rapidity of analysis for question answering. More work is however
needed to confirm these preliminary results by exploring the various
ways analysts use pattern, time, and sequence steering during longer
analysis sessions.
N. Andrienko and G. Andrienko. Exploratory analysis of spatial and
temporal data: a systematic approach. Springer Science & Business
Media, 2006.
M. Angelini and G. Santucci. Modeling Incremental Visualizations. In
M. Pohl and H. Schumann, eds., EuroVis Workshop on Visual Analytics.
The Eurographics Association, 2013. doi: 10.2312/PE. EuroVAST.
J. Ayres, J. Flannick, J. Gehrke, and T. Yiu. Sequential pattern mining
using a bitmap representation. In Proceedings of the Eighth ACM
SIGKDD International Conference on Knowledge Discovery and Data
Mining, KDD ’02, pp. 429–435. ACM, New York, NY, USA, 2002.
S. K. Badam, N. Elmqvist, and J.-D. Fekete. Steering the Craft: UI El-
ements and Visualizations for Supporting Progressive Visual Analytics.
Computer Graphics Forum, 36(3):491–502, June 2017.
M. Brehmer and T. Munzner. A multi-level typology of abstract vi-
sualization tasks. IEEE Transactions on Visualization and Computer
Graphics, 19(12):2376–2385, Dec 2013.
L. A. F. Fernandes and A. C. B. Garc
ıa. Association rule visualization
and pruning through response-style data organization and clustering.
In J. Pav
on, N. D. Duque-M
endez, and R. Fuentes-Fern
andez, eds.,
Advances in Artificial Intelligence – IBERAMIA 2012: 13th Ibero-
American Conference on AI, Cartagena de Indias, Colombia, Novem-
ber 13-16, 2012. Proceedings, pp. 71–80. Springer, 2012.
D. Fisher, I. Popov, S. Drucker, et al. Trust me, i’m partially right:
incremental visualization lets analysts explore large datasets faster. In
Proceedings of the SIGCHI Conference on Human Factors in Comput-
ing Systems, pp. 1673–1682. ACM, 2012.
P. Fournier-Viger, J. C.-W. Lin, A. Gomariz, T. Gueniche, A. Soltani,
Z. Deng, and H. T. Lam. The spmf open-source data mining library
version 2. In B. Berendt, B. Bringmann,
E. Fromont, G. Garriga, P. Mi-
ettinen, N. Tatti, and V. Tresp, eds., Machine Learning and Knowledge
Discovery in Databases, pp. 36–40. Springer, Cham, 2016.
E. Glatz, S. Mavromatidis, B. Ager, and X. Dimitropoulos. Visualizing
big network traffic data using frequent pattern mining and hypergraphs.
Computing, 96(1):27–38, Jan 2014. doi: 10. 1007/s00607-013-0282-8
B. Goethals, S. Moens, and J. Vreeken. Mime: A framework for
interactive visual pattern mining. In Proc. 17th ACM SIGKDD, KDD
’11, pp. 757–760. ACM, 2011.
M. Gupta and J. Han. Applications of pattern discovery using sequential
data mining. In P. Kumar, P. R. Krishna, and S. B. Raju, eds., Pattern
Discovery Using Sequence Data Mining: Applications and Studies,
chap. 1, pp. 1–23. IGI Global, 2012.
M. Hahsler and R. Karpienko. Visualizing association rules in hierar-
chical groups. Journal of Business Economics, 87(3):317–335, Apr
2017. doi: 10. 1007/s11573-016-0822-8
J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M.-C. Hsu.
Freespan: Frequent pattern-projected sequential pattern mining. In
Proc. ACM SIGKDD, pp. 355–359. ACM, New York, NY, USA, 2000.
B. C. Kwon, J. Verma, and A. Perer. Peekquence: Visual analytics for
event sequence data. In ACM SIGKDD 2016 Workshop on Interactive
Data Exploration and Analytics, vol. 1, 2016.
H. Lei, C. Xie, P. Shang, F. Zhang, W. Chen, and Q. Peng. Visual anal-
ysis of user-driven association rule mining. In Proc. 9th International
Symposium on Visual Information Communication and Interaction,
VINCI ’16, pp. 96–103. ACM, New York, NY, USA, 2016.
G. Liu, A. Suchitra, H. Zhang, M. Feng, S.-K. Ng, and L. Wong.
Assocexplorer: An association rule visualization system for exploratory
data analysis. In Proc. 18th ACM SIGKDD, KDD ’12, pp. 1536–1539.
ACM, New York, NY, USA, 2012.
Z. Liu, Y. Wang, M. Dontcheva, M. Hoffman, S. Walker, and A. Wilson.
Patterns and sequences: Interactive exploration of clickstreams to
understand common visitor paths. IEEE Transactions on Visualization
and Computer Graphics, 23(1):321–330, Jan 2017. doi: 10. 1109/
TVCG.2016. 2598797
A. MacEachren. How maps work: representation, visualization, and
design. New York: Guilford Press, 1995.
H. Mannila and H. Toivonen. Discovering generalized episodes using
minimal occurrences. In KDD, vol. 96, pp. 146–151, 1996.
H. Mannila, H. Toivonen, and A. Inkeri Verkamo. Discovery of frequent
episodes in event sequences. Data Mining and Knowledge Discovery,
1(3):259–289, 1997. doi: 10. 1023/A:1009748302351
H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering frequent
episodes in sequences extended abstract. In Proc. first Conference on
Knowledge Discovery and Data Mining, pp. 210–215, 1995.
F. Masseglia, F. Cathala, and P. Poncelet. The psp approach for mining
sequential patterns. In J. ytkow and M. Quafafou, eds., Principles
of Data Mining and Knowledge Discovery, vol. 1510 of LNCS, pp.
176–184. Springer Berlin Heidelberg, 1998.
M. Monroe, R. Lan, H. Lee, C. Plaisant, and B. Shneiderman. Temporal
event sequence simplification. IEEE Transactions on Visualization and
Computer Graphics, 19(12):2227–2236, Dec 2013.
C. H. Mooney and J. F. Roddick. Sequential pattern mining – ap-
proaches and algorithms. ACM Comput. Surv., 45(2):19:1–19:39, Mar.
2013. doi: 10. 1145/2431211.2431218
T. M
uhlbacher, H. Piringer, S. Gratzl, M. Sedlmair, and M. Streit.
Opening the black box: Strategies for increased user involvement in ex-
isting algorithm implementations. IEEE Transactions on Visualization
and Computer Graphics, 20(12):1643–1652, Dec 2014.
J. Pei, J. Han, B. Mortazavi-asl, H. Pinto, Q. Chen, U. Dayal, and
M. chun Hsu. Prefixspan: Mining sequential patterns efficiently by
prefix-projected pattern growth. pp. 215–224, 2001.
V. Raveneau, J. Blanchard, and Y. Pri
e. Toward an open-source tool
for pattern-based progressive analytics on interaction traces. IEEE VIS
2016 Workshop on Temporal and Sequential Event Analysis, Baltimore,
MD, USA, 2016.
M. Seno and G. Karypis. Slpminer: an algorithm for finding frequent
sequential patterns using length-decreasing support constraint. In Data
Mining, 2002. ICDM 2003. Proceedings. 2002 IEEE International
Conference on, pp. 418–425, 2002.
R. Srikant and R. Agrawal. Mining sequential patterns: Generalizations
and performance improvements. In Proc. 5th International Conference
on Extending Database Technology: Advances in Database Technology,
EDBT ’96, pp. 3–17. Springer-Verlag, London, UK, UK, 1996.
C. D. Stolper, A. Perer, and D. Gotz. Progressive visual analytics:
User-driven visual exploration of in-progress analytics. IEEE Trans. on
Visualization and Computer Graphics, 20(12):1653–1662, Dec 2014.
M. van Leeuwen. Interactive data exploration using pattern mining. In
A. Holzinger and I. Jurisica, eds., Interactive Knowledge Discovery and
Data Mining in Biomedical Informatics: State-of-the-Art and Future
Challenges, pp. 169–182. Springer, 2014.
Z. Yang and M. Kitsuregawa. Lapin-spam: An improved algorithm for
mining sequential pattern. In Data Engineering Workshops, 2005. 21st
International Conference on, pp. 1222–1222, April 2005.
M. Zaki. Spade: An efficient algorithm for mining frequent sequences.
Machine Learning, 42(1-2):31–60, 2001.
E. Zgraggen, A. Galakatos, A. Crotty, J. D. Fekete, and T. Kraska. How
progressive visualizations affect exploratory analysis. IEEE Trans. on
Visualization and Computer Graphics, 23(8):1977–1987, Aug 2017.
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Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called InsightsFeed for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression.
Conference Paper
Queries over large scale (petabyte) data bases often mean waiting overnight for a result to come back. Scale costs time. Such time also means that potential avenues of exploration are ignored because the costs are perceived to be too high to run or even propose them. With sampleAction we have explored whether interaction techniques to present query results running over only incremental samples can be presented as sufficiently trustworthy for analysts both to make closer to real time decisions about their queries and to be more exploratory in their questions of the data. Our work with three teams of analysts suggests that we can indeed accelerate and open up the query process with such incremental visualizations.
Conference Paper
Association rules have been widely used for detecting relations between attribute-value pairs of categorical datasets. Existing solutions of mining interesting association rules are based on the support-confidence theory. However, it is non-trivial for the user to understand and modify the rules or the results of intermediate steps in the mining process, because the interestingness of rules might differ largely for various tasks and users. In this paper we propose to reinforce conventional association rule mining process by mapping the entire process into a visualization assisted loop, with which the user workload for modulating parameters and mining rules is reduced, and the mining efficiency is greatly improved. A matrix-based visualization technique is employed to encode the measure computation value, the data distribution and the intermediate results. We also design a set of visual exploration tools to support interactively inspection and manipulation of association measures, constraints of different types, and the results of intermediate steps. The effectiveness of our approach is demonstrated with various scenarios.
The stated goal for visual data exploration is to operate at a rate that matches the pace of human data analysts, but the ever increasing amount of data has led to a fundamental problem: datasets are often too large to process within interactive time frames. Progressive analytics and visualizations have been proposed as potential solutions to this issue. By processing data incrementally in small chunks, progressive systems provide approximate query answers at interactive speeds that are then refined over time with increasing precision. We study how progressive visualizations affect users in exploratory settings in an experiment where we capture user behavior and knowledge discovery through interaction logs and think-aloud protocols. Our experiment includes three visualization conditions and different simulated dataset sizes. The visualization conditions are: (1) blocking, where results are displayed only after the entire dataset has been processed; (2) instantaneous, a hypothetical condition where results are shown almost immediately; and (3) progressive, where approximate results are displayed quickly and then refined over time. We analyze the data collected in our experiment and observe that users perform equally well with either instantaneous or progressive visualizations in key metrics, such as insight discovery rates and dataset coverage, while blocking visualizations have detrimental effects.
Modern web clickstream data consists of long, high-dimensional sequences of multivariate events, making it difficult to analyze. Following the overarching principle that the visual interface should provide information about the dataset at multiple levels of granularity and allow users to easily navigate across these levels, we identify four levels of granularity in clickstream analysis: patterns, segments, sequences and events. We present an analytic pipeline consisting of three stages: pattern mining, pattern pruning and coordinated exploration between patterns and sequences. Based on this approach, we discuss properties of maximal sequential patterns, propose methods to reduce the number of patterns and describe design considerations for visualizing the extracted sequential patterns and the corresponding raw sequences. We demonstrate the viability of our approach through an analysis scenario and discuss the strengths and limitations of the methods based on user feedback.