Information infrastructures become increasingly decentralized. In these environments, coming up with coherent collective decision systems is di cult, especially in environments where central \authorities" are lacking. In this paper, we explore the idea of using artifacts called Blackboards for Decision Tables to support collaborative and incremental evolution of a network of Semantic Decision Tables, which we expect can improve the stakeholders capabilities to make decisions at a local and a community level.
Within this paper, we aim to support a group of stakeholders in their decision making processes, in order to help them to accomplish their goals and maintaining an acceptable degree of coherence in their actions. In our scenario, the stakeholders may have their own inputs, processes and outcomes, which are modeled with business rules through explicit artifacts.
There are several decision support alternatives which aim to support these
kinds of systems. Within this paper we will focus on the use of networks of
simple but powerful artifacts called Semantic Decision Tables [
Through this paper we will focus on how we can represent and interlink the local semantic rules of multiple stakeholders using artifacts which, we believe, can be useful for decision support in distributed environments.
We want to explore the possible bene ts of an environment where multiple stakeholders (i) collaboratively de ne their rules and (ii) build up a network of semantic mappings, in order to reach con gurations that support coherent decisions at community level.
This document is organized as follows: Section 2 presents a brief description of our problem. Section 3 will explore the related work on this subject. Section 4 will provide some de nitions. Section 5 will describe how the decision artifacts are updated. Section 6 will describe the dynamics of this representation mechanism. Section 7 presents our conclusions and future work. 2
Suppose that we count with a group of citizens that move on daily basis to their workplaces. Some of them make use of public transportation consisting of three means: Bus, Tram and Metro. Thanks to shared schemas, this system counts with the necessary infrastructure to provide real-time information on public transportation, e.g. bus schedules, movements. Simultaneously, other citizens transport themselves using a car or bicycles, using information systems with similar characteristics.
Now let's suppose that due to the heavy tra c congestion, a third party provides a Peer to Peer (P2P) network where vehicle owners can o er theirs to others, specifying attributes such as vehicle model, vehicle consumption etc. In the same way, consumers can provide information such as schedules (i.e Use a car Mondays & Thursdays), collaboratively build promises of use etc. Within this system, users are allowed to de ne attributes on their own.
In this example, we show two scenarios with distinct semantic requirements. The former is a system that needs to integrate multiple information sources through the use of a global interchange schema, which can be built by a public transportation authority. The latter is a system that consists of multiple peers interconnected, where we don't necessarily have a central authority. In this scenario we may expect semantics that di er for each peer, for example users may have distinct representation of needs, cultural backgrounds, goals etc. To support the need of heterogeneous semantics in a P2P network, one possibility is to provide shared artifacts, where the peers incrementally try to convey structures to cover their Semantic interoperability requirements.
Naturally, this alone does not solve the collaborative decision making needs. We still need to provide an environment where the peers collectively build up con gurations that support certain coherence at community level. Within the scope of this paper we will explore how the distinct peers can build up a network of SDTs in order to specify conditions to be processed globally. The goal of this is to come up with P2P decisions that are convenient to the community as a whole, for example to diminish the tra c congestion in a city. 3
Because of their simplicity, Decision Tables (DT) are widely used tools to aid in
decision making processes, providing reasoning in a compact form [
SDTs make use of ontologies, which are artifacts that facilitate information
sharing and \understanding" between agents. In IT related domains, an
ontology is understood as a shared, computer stored conceptualization in a formal
language agreed upon a group of stakeholders that enables system
interoperability [
How those ontologies can be constructed will depend heavily on the nature
of the stakeholder ecosystem, and how di cult it is to reach global agreements.
Depending on this, we can adopt distinct strategies to identify concepts
(bottomup, top-down or a combination). In this paper we will focus on systems where
counting with \global interoperability" is di cult, and we regard interoperability
as emerging from collections of incremental agreements between autonomous
agents [
Web Blackboards are dynamic data-spaces that can be traced along with their community of use. Stakeholders are free of creating and joining an arbitrary number of blackboards within their network, where they contribute to the models they describe in order to express for example, business rules. The architecture of such networks aims to provide a playground where local agreements are organically constructed in decentralized ways, building networks of blackboards via interlinking. The main characteristics of those networks are: (i) divergence and convergence capabilities in order to admit heterogeneity, and (ii) full traceability to support high-level interactions such as collective decisions. 4
In this section, we will provide some of the de nitions that will support a system of this nature. We will provide de nitions of some basic artifacts that will be used to collaboratively de ne a network to support collaborative decisions. De nition 1 (Decision Table). A decision table DT is a triple (C; A; F ) where (C) is a set of conditions, A is a set of actions and F is a set of decision rules as de ned below. { A condition c(c 2 C) is tuple (cs; ce) where cs(cs 2 CS) is a condition stub (label) and ce(ce 2 CE) is a condition entry (a value or value range). { An action a(a 2 A) is a tuple (as; ae) where as(as 2 AS) is an action stub (label) and ae(ae 2 AR) is an action entry (a value). { A decision rule f 2 F is a function f : CEcs ! A where CEcs as usual denotes the complete set of assignments of CS to CE.
If we use L to denote the set of labels then we get L = CS [ AS. De nition 2 (Semantic decision table). A semantic decision table SDT is a triple (DT; T; A) where DT is a decision table, T is a nite set of ontological annotation and A is a nite set of asserted axioms
Given an ontology and concepts C1; C2; ; Ck that are de ned in , an ontological annotation t(t 2 T ) is a relation between a label l(l 2 L) and Ci(1 i k), and denoted as t(l; Ci)
t is used (but not limited) to describe the following semantic relationships: instance-of/type-of, subtype/supertype, equivalent.
We can further specify A into two parts and denote it as A A0 [ A00, where A0 is part of ABox of domain ontologies that are used for the annotation and A00 is a set of axioms describing the meta-information of the DT . We get A0 by searching the relevant axioms in the ontology using T .
De nition 3 (Blackboard). A model space is a nite set of models. Given a model space , a set of participants p, a shared ontology and a uniform resource identi er U RI, a blackboard B is a quadruple (e; p; ; U RI).
can be any imaginable and implementable model, such a data model, decision model, a rule or business model. In this paper, we will use semantic decision tables as the studied models. U RI is a uniform resource identi er
A blackboard can be used for constructing a single SDT , establishing dependencies between two SDT s or aligning two ontologies.
De nition 4 (Blackboard for decision tables). A blackboard for semantic decision table Bsdt is de ned as a quadruple (SDT; p; ; U RI) where SDT is a semantic decision table that is represented on the blackboard, p = p1; p2; ; pn is a set of participants, is the shared ontology, U RI is a uniform resource identi er
We use a dot to indicate the owner of an element, e.g., if we have a semantic decision table SDT1, then the condition stub set from SDT1 is denoted as SDT1 CS.
De nition 5 (Blackboard for specifying SDT dependencies). Given two semantic decision tables SDT1 and SDT2, a blackboard for specifying SDT dependencies Bscdt is de ned as a quadruple (SDT; p; ; U RI) where SDT is a set of dependencies between SDT1 and SDT2, p is a set of participants, is the shared ontology and U RI is a uniform resource identi er. Possible dependencies are illustrated as follows: { d1: label mapping, which is a mapping instance between SDT1 L and SDT2 L; A dependency of label mapping is denoted as d1(d; d0) where d 2 SDT1 L and d0 2 SDT1 L { d2: input and output mapping, which is a 1 : 1 mapping instance between SDT1 CS and SDT2 AS, and between SDT1 AS and SDT2 CS; A dependency of input and output mapping is denoted as d2(dc; da0) and d2(da; dc0) where dc 2 SDT1 CS, da0 2 SDT2 AS, da 2 SDT1 AS and dc0 2 SF T2 CS . . . 5
How to manage changes within SDTs may depend largely on the dependencies between them. Take the following example, where SDTs are de ned by di erent stakeholders (or group of stakeholders). As shown in Fig. 3, Stakeholder 1 commits to SDT1 and SDT2, Stakeholder 2 commits to SDT1, SDT2, SDT4 and SDT5, while Stakeholder 3 commits to SDT5 and SDT6. It is common that SDTs are dependent on each other no matter whether or not they come from the same organization.
When a stakeholder wants to make use of a SDT, he may join to the blackboard that holds its representation, if this stakeholder wants to make modi cations there are two ways - one is to clone the SDTs and directly perform the modi cations within the new variant, or start a modi cation agreement dialog with the other stakeholders that commit to the same SDT.
Each time that a Bsdt is cloned, we will have a new derived Bsdt that will be the result of a change with respect to the previous one via a set of change operators applied sequentially by the stakeholders. Below is a list of preliminary valid change operators within a single SDT variant.
1 : rename condition stubs, condition entries, action stubs and action entries 2 : update decision rules 3 : update condition/action entries 4 : remove conditions and actions 5 : add conditions, actions and decision rules 6 : add new axioms 7 : reinterpret the table content by annotating with another domain ontology 8 : update dependency relations
The choice of the change operators to be used in each group decision system
will depend on the application requirements and is a matter of design. For
example some applications may use very granular ones, such as update decision
rule or add new axiom, while others may prefer execute change operators such
as extend bus schedule which can be constructed via composition of more
granular ones via layered operator frameworks [
In the same way, the incremental and collaborative change in a network of Bsdt will result in a increasing map of variants that is a network.
In Fig. 4 we present a simpli ed diagram that shows how a small set of stakeholders deal with multiple Bsdt variants. In this situation, Each Bsdt variant is identi ed globally and can be branched from an ancestor Bsdt or merged with other branches, de ning distinct sets of conditions and actions. Each time that a ancestor variant merges or branches, the applied sequence of change operators is registered and identi ed forming a traceability unit that may include pointers to deltas between two states, discussions between the involved stakeholders, updates to dependencies between Decision Tables etc. Some operations may not need branching or merging of Bsdt variants, such as terminology renaming, which can be considered as annotations of the underlying ontology.
To support these branching and merging capabilities, and to represent the variants evolution, we adopt a direct acyclic graph model (DAG). This DAG allows us to represent the i) branching an Bsdt variant ii) merging of two variants of with common ancestor. iii) recording the traceability information such as ancestors or sequence of change operators with respect to those ancestors.
This provides a strong support for non-linear SDT development with an approach that has already been proven successful in other elds (e.g., collaborative software development with version control systems such as GIT1. Within this approach we allow multiple SDTs to coexist, improving exibility and scalability, but at the cost of a higher management complexity. 1 http://gitscm.com
Through work, we explored the notion of decentralized SDT development through Bsdt networks. We expect that these kinds of layouts are useful to provide coherent decisions at community level in environments where we don't necessarily count with central authorities. This leaves us space for further questions, such as: (i) What should be the agreement mechanisms between the stakeholders that commit to a Bsdt? (ii) How we can pro t from Bsdt networks to propagate, group and execute distinct SDT to improve collaborative decision making? (iii) How these networks evolve, and how they can be observed by the stakeholders increasing their awareness? (iv) How we can pro t from the traceability of Bsdt networks, to provide insight that support cooperative social \order"? 7.1
Acknowledgments The research described in this paper was partially sponsored by the INNOViris Open Semantic Cloud for Brussels (OSCB) project.