=Paper=
{{Paper
|id=None
|storemode=property
|title=Augmented Collaborative Spaces for Collective Sense Making: The Dicode Approach
|pdfUrl=https://ceur-ws.org/Vol-743/ASTC2011_Paper2.pdf
|volume=Vol-743
}}
==Augmented Collaborative Spaces for Collective Sense Making: The Dicode Approach==
https://ceur-ws.org/Vol-743/ASTC2011_Paper2.pdf
Augmented Collaborative Spaces for Collective Sense
Making: The Dicode Approach
Ahmad Ammari1, Vania Dimitrova1, Lydia Lau1, Manolis Tzagarakis2, and Nikos
Karacapilidis2
1
School of Computing University of Leeds, Leeds, UK.
2
Research Academic Computer Technology Institute, Patras, Greece.
{ A.Ammari, V.G.Dimitrova, L.M.S.Lau, }@leeds.ac.uk, tzagara@upatras.gr,
nikos@mech.upatras.gr.
Abstract. Sense making is at the heart of cognitively complex and data
intensive decision making processes. It is often conducted in collective spaces
through exchange of ideas, discussions, analysing situations, and exploring
alternatives. This position paper proposes a novel approach to facilitate
collective sense making via a collaboration platform which (a) offers multiple
views to collaboration (including forums, mind maps, and argumentation
structure), and (b) provides intelligent support to understand sense making
behaviour by employing user and community modelling techniques. The work
is conducted in the framework of the EU funded Dicode project, developing
intelligent services for data-intensive collaboration and decision making.
Keywords: Collective sense making, Collaborative workspaces, Intelligent
support, User and community modelling
1 Introduction
This paper proposes a novel platform to augment the synergy between human and
machine intelligence in complex decision making situations. Many collaborative
decision making problems have to be solved through dialoguing and argumentation
among a group of people [1, 2]. In such contexts, discussions for making sense of the
issues, constraints, and options are usually conducted in an unstructured manner.
Sense making is a “motivated, continuous effort to understand connections (which
can be among people, places, and events) in order to anticipate their trajectories and
act effectively” [3]. Therefore, sense making is an inevitable path in cognitively
complex and data intensive decision making processes.
Dicode1 (Data-intensive collaboration and decision making), an EU Framework 7
project, sets out to tackle the above challenges for three use cases. The first use case
concerns a team of scientists in clinico-genomic research. The second use case
involves a group of radiographers, radiologists and clinicians in a trial of rheumatoid
arthritis treatment. The third use case involves public opinion monitoring on the
internet for a team of brand consultants to design a campaign.
1
Dicode website is http://dicode-project.eu/
4 A. Ammari et al.
Argumentation, as seen in Dicode, is a common activity in collective sense making
process. It is valuable in shaping a common understanding of the problem and can
provide the means to decide which parts of the information brought up by the decision
makers will finally be the input to the solution used. Argumentation may also
stimulate the participation of decision makers and encourage constructive criticism.
However, discovering the connections is mainly by using tacit knowledge and the
value of this activity has been largely unacknowledged. Dicode aims to address the
above by user-friendly multi-view collaboration workspaces, which facilitate the
exchange and sharing of ideas, opinions, comments and resources between
participants. While each collaborative workspace enables an individual or a team to
visualise the connections between concepts and artefacts, keeping track of the
rationale behind the decision points and redeploying the accumulated knowledge in
new situations is itself potentially a cognitively complex process. Hence, intelligent
support will be provided by exploiting the behaviour data captured in the usage logs
and by adding semantics to the content shared.
This position paper outlines a multi-faceted approach to combine human and
machine intelligence for collective sense making. Specifically, we will present a novel
approach to design collaborative workspaces that facilitate sense making by
combining multiple views – ranging from informal (unstructured) to formal
(structured). Each view facilitates different sense making aspects. Furthermore, we
present a proposal how collaborative workspaces can be augmented with intelligent
support utilising adaptation techniques, namely user and community modelling.
2 The Dicode Project
The goal of the Dicode project is to facilitate and augment collaboration and decision
making in data-intensive and cognitively-complex settings. It will exploit and build
on the most prominent high-performance computing paradigms and large data
processing technologies - such as cloud computing, MapReduce [4], Hadoop2,
Mahout3, and column databases – to meaningfully search, analyze and aggregate data
existing in diverse, extremely large, and rapidly evolving sources. Building on current
advancements, the solution foreseen in the Dicode project will bring together the
reasoning capabilities of both the machine and the humans. It can be viewed as an
innovative workbench incorporating and orchestrating a set of interoperable services
that reduce the data-intensiveness and complexity overload at critical decision points
to a manageable level, thus permitting stakeholders to be more productive and
concentrate on creative activities. Services to be developed are: (i) scalable data
mining services (including services for text mining and opinion mining), (ii)
collaboration support services, and (iii) decision making support services.
In this paper, the focus is on the collaboration support services which are realised
via multi-view collaborative workspaces augmented with intelligent support for
collective sense making.
2
Apache Hadoop Project http://hadoop.apache.org/
3
Apache Mahout Project http://mahout.apache.org/
Augmented Collaborative Spaces for Collective Sense Making: The Dicode Approach 5
3 Multi-View Collaborative Workspace
In Dicode, three different views of collaboration workspaces (CW) are supported.
These are summarised below:
• Discussion-forum view: In this view, the CW is displayed as a traditional web-
based forum, where posts are displayed in an ascending chronological order. Users
are able to post new messages to the collaboration workspace, which appear at the
end of the list of messages. Posts may also have attachments to enable the
uploading of files. Discussion-forum exhibits a very low level of formality and are
mainly suitable to support ideas sharing, exchange and collection.
• Mind-map view: In this view, the CW is displayed as a mind map where users can
interact with the items on the collaboration workspace. This view deploys a spatial
metaphor permitting the easy movement and arrangement of items on the
collaboration workspace (Fig. 1). Messages posted on the collaboration workspace
in mind-map view can be one of the following types: idea, comment, note and
generic. Files of any content type (e.g. pdf, jpg) can be uploaded to the CW. The
set of available types can be configured and participating users will be able to
define new ones. The mind-map view also provides a set of mechanisms through
which: (a) items on the collaboration workspace can be related, and (b) new
abstractions can be created. In particular, creation of relationships between items is
facilitated by drawing directed arrows between items on the collaboration
workspace. Visual cues can be used to convey semantics (e.g. red colour can
indicate opposition, while green can indicate “in favour”; labels can be associated
to arrows elucidating semantic relationships). Items on the CW can be aggregated,
to allow a group of items to be treated as a single entity, and transformed into a
single item creating new, composite items. The mind-map view aims at supporting
sense-making during data intensive and cognitive complex tasks.
Fig. 1: Mind-map view of a collaboration workspace. Explicit relations can be created between
collaboration items (arrows) or juxtaposed to express implicit/transient relationship.
• Formal/Argumentation view: The formal/argumentation view of the CW permits
only a limited set of discourse moves for a limited set of message types whose
semantics is predefined and fixed. Formal views of the collaboration workspaces
6 A. Ammari et al.
exhibit a high level of formality. In particular, the formal view (Fig. 2) enables the
posting of messages which can be of type issue (to indicate the decisions to be
made) alternative (to represent potential solutions to the issues discussed) or
position (to comment on alternatives or on other positions). Positions either support
or are against alternatives and positions and their relationship are explicitly
specified when users post them to the collaboration workspace. Files can be
attached to positions to further support their validity. The formal view supports
also the notion of preferences, used to weigh the importance of two positions and
reflect the importance of one position over another. Decision making support
algorithms (e.g. a voting or a multiple criteria decision making), which are
associated with the CW, can take into consideration the relationships of positions
as well as existing preferences and calculate which alternative is currently
prevailing or which position has been defeated. The aim of the formal view is to
make the CW machine understandable and to further support decision making.
Fig. 2: A formal view of the collaboration workspace shown in Fig. 1.
Every CW can be transformed from one view into another at any point in time
by anyone participating in the collaboration. Such transformations are rule-based; a
set of rules specifies how items in the source view are transformed into items of the
destination view. All discourse moves and contributions that users create during
their interaction in the CW are logged within Dicode in order to enable their further
analysis by a variety of services. For each view, log data contains information related
to the event that happened on the workspace and which includes:
• the collaboration workspace’s ID and view where the event took place;
• the user’s operation and the associated content (e.g. adding/updating/deleting an
item, moving an item, creating relationships between items etc);
• the user who executed the operation;
• the date and time when the event occurred.
The log data in the CW will be used as an input for intelligent support algorithms.
Augmented Collaborative Spaces for Collective Sense Making: The Dicode Approach 7
4 Intelligent Support
Intelligent support will augment the multi-view CWs with machine intelligence to
understand and facilitate collective sense making. Intelligent support will be provided
at two levels:
• Understanding collective sense making. This will include user/community
profiling, e.g. identifying user characteristics, discovering links between
individuals, identifying common topics; discovering patterns of behaviour such as
silos or dominance, extracting situations parameters.
• Facilitating collective sense making. This will include interface augmentation (e.g.
adding visual signals to help establish situational awareness) or suggestions in the
form of messages (e.g. to facilitate the exchange of ideas, point at useful patterns,
highlight important situation aspects).
The following subsections propose our approach to implementing the first level of
intelligent support, i.e. understanding collective sense making behaviour. This will be
achieved by three functions (section 4.3) which employ descriptive machine learning
and data mining algorithms and meet the key objectives as stated in section 4.2. The
following section outlines how the CW log data will be enriched with semantics for
user and community modelling.
4.1 Input: Augmented CW Log Data
Intelligent support will be based on the log data from the CWs which include mind
mapping graphs, discussions, arguments and comments. In addition, the users’ meta-
data, including the users’ navigational behaviour as recorded in the usage logs, as well
as the searching behaviour of the users in the collaborative workspace, will be used to
characterise the users and derive a user profile for each user in the community.
Semantic enrichment of the user profiles is achieved by considering semantic data
sources, such as domain ontologies (to identify the domain topics discussed), as well a
collaboration and decision making ontology developed in Dicode (to take into account
the user roles and to link sense making to decision making steps).
4.2 User and Community Modelling
Intelligent support in Dicode is underpinned by a mechanism for user and community
modelling which will be outlined here. It is envisaged to be used by intelligent
services which augment the CW in Dicode. For instance, a recommendation
mechanism in Dicode will be able to use the output of the community modelling
functions to direct to ‘items’ in the CW, e.g. a data set, a set of relevant discussions, a
topic of interest to search for. Furthermore, the users of the CW can be pointed to a
set of discussions that occurred in different times but belong to a certain topic of
interest.
Objectives. The following four main objectives can be perceived for the
community modelling and user profiling functions:
8 A. Ammari et al.
• O1: Detect topics of community discussions in the collaborative workspace.
• O2: Identify key characteristics of the users in the community from available data
about the users, i.e. unstructured data, semantic annotations, meta-data, and use
these characteristics to shape the user profile for each user within the community.
• O3: Quantify the strength of each characteristic for discovery of connections.
• O4: Discover clusters of users and interesting patterns in user behaviour by
applying descriptive data mining functions, i.e. cluster analysis and association
mining on the derived user profiles.
4.3 Outline of the Main Algorithms
This section will outline how descriptive machine learning and data mining, such
as cluster analysis and association rule mining, can be applied for user and
community modelling. We will group them into three main functions.
Function 1: Clustering Unstructured Data for Topic Detection
Purpose (O1). The main purpose of this function is to discover the main topics of
the unstructured data, i.e. community discussions, arguments, using descriptive data
mining methods, i.e. cluster analysis.
Input. Unstructured data that community users create within the collaborative
workbench, as part of their collaboration activities. These include the discussion and
arguments that occurred between the community users in the workbench. All the
available parts of the discussions can be utilized by the function, i.e. the title of the
discussion thread, main discussion body, replies by other users, tags that collaborating
users attach to the discussion.
Processing. The input data will be processed as follows:
• Pre-process the input unstructured data and transform it into a term weight
document matrix to be used as input for cluster analysis.
• Using the pre-processed matrix, build and train a clustering model that segments
the discussions into distinct groups (clusters) based on the similarities and
distances between the discussions.
• Using the profiles of the discovered clusters, detect the topic of each cluster of
discussions based on the frequency of occurrence by considering the most
occurring terms that occur in each cluster.
Output. There are two types of output produced by this function:
• Clusters of discussions, where each discussion instance will be assigned a cluster
id to identify to which discovered cluster of discussions it belongs to.
• Cluster profiles, including the number of discussions that belong to each cluster
and the most significant terms that belong to each cluster based on the frequency of
occurrence.
Function 2: Deriving Key User Characteristics and Generating User Profiles
Purpose (O2 & O3). The purpose of this function is to derive the key
characteristics that describe each user within the community, and weight these
Augmented Collaborative Spaces for Collective Sense Making: The Dicode Approach 9
characteristics for every user to reflect the significance of each characteristic. These
weighted user profiles will be accumulated in a community model.
Input. Data input to this function include: (a) Discussion topics that are detected
using the first function described above; (b) User meta-data available from the logs
and meta-data derived from the other components of the collaborative workbench,
including the discussions, arguments, i.e. the author of the main body of the
discussion and the authors of the replies to the main body, the mind mapping graphs,
and the meta-data available from the searching behaviour in the workspace. (3) The
characteristics derived from the unstructured data, i.e. topics, and the meta-data can
be semantically enriched by the collaboration and decision support ontology, relevant
domain ontologies, and open lexical resources, i.e. Wordnet.
Processing. This function will process the input data as follows:
• Identify user characteristics within the community from the available input data.
• Compute weighted interests in the identified topics - for each identified
characteristic, the function will compute a numerical weight for each user profile
that represents the significance (importance) of this characteristic to that user
within the community.
• Build a user–characteristic matrix that could be input to further descriptive data
mining functions (cluster analysis and association mining).
Output. The output of this function is a community model that includes a user
profile for each user. Each user profile represents the weights of the identified
characteristics for each user within the community.
Function 3: Discovering Patterns in the User Profiles
Purpose (O4). The purpose of this function is to discover hidden patterns in the
user profiles for further support to collaboration and decision making, using
descriptive data mining techniques.
Inputs. The input to this function is mainly the community model (user profiles)
derived by the second function
Processing. This function will process the input data as follows:
• Apply cluster analysis methods on the derived user profiles within the community
model to discover the user clusters and the user cluster profiles.
• Apply association mining methods on the derived user profiles within the
community model to discover association hidden patterns within the user
characteristics.
Output. This function mainly produces three outputs: (a) Clusters of user profiles,
where each user profile instance will be assigned a cluster id to identify to which
discovered cluster of user profiles each user belongs to. (b) Cluster profiles, including
the number of user profiles that belong to each cluster and the characteristics’ values
for the average user profile, i.e. cluster centroid, for each discovered cluster. (c)
Discovered hidden association patterns, including frequent characteristic-sets that list
those significant characteristics that are obtained frequently by the same users, and the
hidden association rules underlying these sets.
10 A. Ammari et al.
5 Related Work
The approach proposed in this paper has two main innovative aspects: (a) a new way
to facilitate sense making using multiple linked views of collaborative workspaces;
and (b) a novel application of user and community modelling to get an understanding
of collective sense making behaviour.
Over the years, a number of systems have been developed aiming to support the
process of sense making which include Debatepedia [5], Parmenides [6], ClaiMaker
[7], TruthMapper [8] and Cohere [9]. Despite their powerful features, each of these
systems provides only a fixed level of formality lacking the ability to adapt their
environment to the needs of the collaboration. In Dicode, collaborative workspaces
build on and extend the notion of spatial hypertext, which has been proposed as an
alternative to navigational and semantic organisation of resources [10]. Spatial
hypertext employs a spatial metaphor to organize information aiming at taking
advantage of the user’s visual memory and pattern recognition. Due to its ability to
express ambiguity as well as transient and implicit relationships between information,
it is an effective way to support information triage, i.e. the process of sorting through
relevant materials and organizing them to meet the needs at hand[11]. While most
existing hypertext systems permit only a single user to organize the information (e.g.
VIKI [12], WARP [13]), approaches to bring spatial hypertext into the collaborative
realm have only recently started to emerge [14]. Dicode will make a contribution to
this stream by exploiting spatial hypertext for collective sensemaking in cases when
humans need to process large volumes of heterogeneous data.
Recent research trends look at intelligent ways to support the effective functioning
of close-knit communities through personalization and adaptation techniques.
Modelling users within a community provides the grounds for generating group
recommendations [15]. One method to support that is through detecting the topics that
the collaborating users show interests in. In [16] Cheng and Vassileva derived topics
of users’ interests based on the resources shared by them within the community,
where a reward factor is calculated to measure the relevance of each contributed
resource to the topics derived. In [17], Bretzke and Vassileva modelled users’
interests based on how frequently and recently users have searched for a specific area
from a particular taxonomy. User relationships are then determined based on the
resource downloading behaviour. A more recent approach by Kleanthous and
Dimitrova [18][19] employs the metadata of the shared resources along with an
ontology representing the community context and derives a semantically relevant list
of interests for every user.
In Dicode, we aim to further enhance the existing topic detection approaches by
exploiting a hybrid machine learning, text data mining, and semantic enrichment
approach. Using as input community discussions, mind-mapping activities, and
relevant ontologies, we aim to discover topics of interests that are buried within the
diversity of unstructured and semi-structured contents produced by the collaborating
members in the multi-view collaborative workspaces. Detected topics will then be
exploited to facilitate collective sense making within the community members.
A community model can be analysed to automatically detect patterns which can be
used to decide when and how interventions to the community can be done [20]. It has
been shown that community patterns based on these processes can be derived from
Augmented Collaborative Spaces for Collective Sense Making: The Dicode Approach 11
the community graph. For example, [19] have identified community patterns related
to processes linked to effective knowledge sharing, such as transactive memory (how
members’ knowledge is related), shared mental models (shared understanding of the
common goal), and cognitive centrality (influential members).
Similarly to Kleanthous and Dimitrova’s work on semantically-enriched
relationship detection, we will exploit semantics and ontologies to enhance the log
data from CWs and get richer input about what is happening in the community.
However, the community modelling approach in Dicode will take the modelling
further by exploiting descriptive data mining approaches, including output from (i)
statistical member segmentation, i.e. group profiles, where members assigned to the
same group share a similar behavioural profile, as well as output from (ii) association
rule mining, i.e. lists of the frequently co-occurring behavioural activities of the
community members, in order to further improve the community pattern discovery
tasks. Discovered patterns will also be used to further augment the multi-view CW for
enhanced collective sense-making, knowledge sharing, and group recommendations.
6 Conclusions
We have set out an ambitious goal to exploit the synergy of machines and humans in
complex cognitive situations that require making decisions involving large volumes
of data. We are starting to unravel the aspects of this synergy. While data mining
techniques (i.e. machine intelligence) can be exploited to process data and discover
trends and patterns, human intelligence is needed to make sense of the data and take
decisions. The process of sense making involves discovering connections, deriving
patterns, generating alternatives, weighting possibilities. People perform these tasks in
an intuitive manner using tacit knowledge. Our ultimate goal is to capture, preserve,
and reuse this tacit knowledge by providing collaborative workspaces for collective
sense making. In turn, we will exploit machine intelligence to analyse the human
behaviour in the collaborative spaces in order to get a better understanding of the
collective sensemaking process, facilitate important aspects, and support future
human sense making (e.g. exploiting patterns applied earlier).
Currently, we are developing the CWs following a generic approach, which will
enable the same approach to be applied to diverse use cases. The illustrations in this
paper were from the exemplification of the multi-view space for a Breast Cancer
research group embarking on an analysis to discover any common characteristics or
trends that could be deducted from recent studies which used high-throughput
technologies such as microarrays and next-generation sequencing. We plan to apply
the approach presented here to support sense making in a clinical trial of Rheumatoid
Arthritis treatment where a team of medical practitioners examines large data sets and
analyses the effectiveness of the treatment on patients. In addition, the log data from
the CWs is being analysed in line with the functions presented in here to augment
CWs with intelligent support.
12 A. Ammari et al.
Acknowledgements
This publication has been produced in the context of the EU Collaborative Project
"DICODE - Mastering Data-Intensive Collaboration and Decision" which is co-
funded by the European Commission under the contract FP7-ICT-257184. This
publication reflects only the author’s views and the Community is not liable for any
use that may be made of the information contained therein.
References
1. van Eemeren, F. H., Grootendorst, R., Snoeck Henkemans.F.: Fundamentals of
Argumentation Theory. Erblaum, Mahwah, NJ (1996)
2. Provis, C.: Negotiation, Persuasion and Argument. Journal of Argumentation, 18(1),
pp. 95-112 (2004)
3. Klein, G., Moon, B., Hoffman, R.: Making Sense of Sensemaking 1: Alternative
Perspectives. IEEE Intelligent Systems, 21(4), July/August (2006)
4. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. In
OSDI’04: Proceedings of the 6th conference on Symposium on Opearting Systems
Design & Implementation, USENIX Association, pp. 10–10 (2004)
5. Debatepedia, http://wiki.idebate.org/
6. Atkinson, K., Bench-Capon, T.,McBurney, P.: PARMENIDES: Facilitating
deliberation in democracies. Artificial Intelligence and Law, 14(4), pp. 261-275
(2006)
7. Buckingham Shum, S.J., Uren, V., Li, G., Sereno, B., Mancini, C.: Modelling
Naturalistic Argumentation in Research Literatures: Representation and Interaction
Design Issues. International Journal of Intelligent Systems, 22 (1), pp.17-47 (2007)
8. TruthMapping, http://truthmapping.com
9. Buckingham Shum, S.,: Cohere: Towards Web 2.0 Argumentation, Proceeding of the
2008 conference on Computational Models of Argument, p.97-108, June 21 (2008)
10. Shipman, F. M., Marshall, C. C.: Spatial Hypertext: An Alternative to Navigational
and Semantic Links, ACM Computing Surveys 31(4), December (1999)
11. Marshall, C.C., Shipman, F. M. III: Spatial hypertext and the practice of information
triage, Proceedings of the eighth ACM conference on Hypertext, p.124-133, April 06-
11, Southampton, United Kingdom (1997)
12. Marshall, C.C., Shipman, F. M., Coombs, J. H.: VIKI: spatial hypertext supporting
emergent structure, Proceedings of the 1994 ACM European conference on
Hypermedia technology, p.13-23, September 19-23, Edinburgh, Scotland (1994)
13. Francisco-Revilla, L., Shipman, F. M.: WARP: a web-based dynamic spatial
hypertext, Proceedings of the fifteenth ACM conference on Hypertext and
hypermedia, August 09-13, Santa Cruz, CA, USA (2004).
14. Solís, C., Ali, N.: ShyWiki - A spatial hypertext wiki, Proceedings of the 4th
International Symposium on Wikis, September 08-10, Porto, Portugal (2008)
15. Masthoff, J.: Group Modeling: Selecting a Sequence of Television Items to Suit a
Group of Viewers, User Modeling and User-Adapted Interaction, 14(1), pp.37-85
(2004)
16. Cheng, R., Vassileva, J.: Design and evaluation of an adaptive incentive mechanism
for sustained educational online communities, Journal of User Modeling and User
Adaptive Interaction, vol. V16, no. 3, pp.321 – 348 (2006)
17. Bretzke, H., Vassileva, J.: Motivating Cooperation on Peer to Peer Networks, 9th Int.
Conf. on User Modelling, Springer (2003)
Augmented Collaborative Spaces for Collective Sense Making: The Dicode Approach 13
18. Kleanthous, S., Dimitrova, V.: Modelling Semantic Relationships and Centrality to
Facilitate Community Knowledge Sharing, Proc. of the 5th Int. Conf. on Adaptive
Hypermedia and Adaptive Web-Based Systems (AH'08) Springer (2008).
19. Kleanthous, S., Dimitrova, V.: Analyzing Community Knowledge Sharing Behavior,
UMAP 2010, Springer, pp.231-242 (2010)
20. Kleanthous, S., Dimitrova, V.: Detecting Changes over Time in a Knowledge Sharing
Community, Proc. of the 2009 IEEE/WIC/ACM Int. Joint Conf. on WI and IAT,
IEEE Computer Society Washington, DC, USA Milan, Italy (2009).