Degas: A Database of Autonomous objects

In this paper we introduce DEGAS (Dynamic Entities Get Autonomous Status), an active temporal data model based on autonomous objects. The natural combination of active and temporal databases is discussed. The active dimension of DEGAS means that we define the behaviour of objects in terms of production rules. The temporal dimension means that the history of an object is included in the DEGAS data model. Further novel features of DEGAS are the encapsulation of the complete behaviour of an object, both potential and actual. Thus, DEGAS combines dynamic and structural specifications in one model. In addition, DEGAS allows easy evolution of object capabilities through a clear distinction between inherent types and capabilities that can be acquired and lost. This addon mechanism makes DEGAS very suitable as a formalism for role modelling. Finally, the rule model in DEGAS is both simple, through the use of finite automata, and general, because it allows different strategies for dealing with constraints and reacting to events in other objects

Designing active objects in DEGAS

This report discusses application design for active databases, in particular for the active object-based database programming language DEGAS. In DEGAS one modularisation principle, the object, is applied to all elements of the application, including rules. We discuss a design process consisting of four phases, corresponding with the four kinds of capabilities in a DEGAS object, attributes, methods, rules, lifecycles. The elements of this design process are similar to those found in a design methodology such as OMT. To illustrate the design process we use the example of workflow management. In addition, it shows that the application of one modularisation to all elements of an active database leads to a clear modularisation of the workflow application, Furthermore, this modularisation facilitates all important workflow evolutions

Object Histories as a Foundation for an Active OODB

Several links exist between active and temporal databases. These are summarised by the observation that rules are triggered by a specified evolution of the database. In this paper, we discuss the relation between active and temporal database using DEGAS, an object-based active database programming language. To achieve full active database functionality, a DEGAS object records its complete history. Hence, all data needed for a temporal database supporting a single temporal dimension is provided. Furthermore, the semantics of the active behaviour of DEGAS are defined straightforwardly in terms of the object history. Finally, we discuss the advantages and disadvantages of extending DEGAS with a second time dimension (to achieve full temporal functionality) from an active database perspective

Maturity of Operational Procurement in the Construction Industry: A Business/IT-Alignment Perspective

Project execution in the construction industry faces major challenges, e.g. difficulty in coordination and cooperation. Operational procurement during project execution is no exception. In this paper we construct a maturity model, based on earlier work, consisting of six dimensions (goal, control, process, organization, information, technology) and five maturity stages (transactional-oriented, commercial-oriented, coordination, internal-optimized, external-optimized). The model can be used to determine the level of procurement maturity for each of the dimensions, and for the determination of a strategy for growth in the construction industry. With input from a major construction firm in the Netherlands, through simulating tooling, the model is evaluated for its contribution to growth in operational excellence. Results of the simulation show support for a relation between maturity growth and increased operational excellence

DEGAS : a temporal active data model based on object autonomy

This report defines DEGAS, an advanced active data model that is novel in two ways. The first innovation is object autonomy, an extreme form of distributed control. In comparison to more traditional approaches, autonomous objects also encapsulate rule definitions to make them active. The second innovation of DEGAS is its temporal aspect. Active databases have an inherent temporal element in the specification and detection of event patterns that trigger rules. Autonomous objects, the foundation of the DEGAS data model, are independent processes. Their definition includes their complete behaviour, both potential behaviour in the form of methods and lifecycles and actual behaviour in the form of active rules. Relations between objects are objectified. The specialisation mechanism provided by DEGAS is a clean addon mechanism well suited to model dynamic evolution of objects in conjunction with relations. In this report we give a full syntactic and semantic definition of the data model. The state of an autonomous object includes its complete history. This allows the active behaviour of the object to be defined in a purely local way

A data model for autonomous objects

Developments in distribution and networking of computing power raise questions about the feasibility of centralised control in an information system. At the same time the move towards information systems ranging over a number of different organisations brings us systems where central control is not desired. This asks for systems built of components that can function independently under local control only. A move towards autonomous components can also clearly be seen in the area of active databases. Rules are seen as part of the behaviour of objects or relations between objects. Following the encapsulation principle, this leads to encapsulation of all rules with objects. The definition of such an object is independent of other objects. To support these developments we defined the data model for autonomous objects proposed in this report. An autonomous object is an object with its own thread of control. The behaviour of an autonomous object is defined by methods, rules and dynamic constraints. The latter two refer to the complete history kept with the object. The semantics of a relation between objects is captured in relation objects. This enables us to represent arbitrary complex conditions on initiating and terminating relations and to have arbitrary actions taken on certain events. Dependent on the relations an object has, its capabilities will evolve. This is achieved through addons. An addon defines capabilities an object can be, temporarily, extended with. Structure is brought into the mass of objects at the instance level through the objects at the class level. Class objects occur for all object classes, relation object classes and addons. Their function as an object container enables the approach of groups of objects, for example for queries

Towards Congestion Management in Distribution Networks:a Dutch Case Study on Increasing Heat Pump Hosting Capacity

The current high gas prices motivate end-users to replace their gas heating with electric heat pumps. This will likely cause frequent congestion issues in low-voltage (LV) distribution grids and slow down the heat pump adoption rate. To avoid or defer the expensive and complicated grid expansion, this study shares a solution approach of a Dutch Distribution System Operator (DSO) to enable the increasing adoption of heat pumps in existing dense housing areas. Data of the DSO and a local housing company have been combined to investigate the heat pump hosting capacity on a dense urban LV feeder, including realistic data of grid topology, load and heat dynamics, and practical operating characteristics of heat pumps. Our simulation compares two control strategies: (1) individual peak shaving and (2) central optimal power flow control. We show the central optimal power flow control with end-users' thermal comfort constraints and an objective function of minimizing losses can smoothen total grid loading and lead to flat voltage profiles. This allows the approach to be robust against baseload forecast errors, while the individual peak shaving is more prone to such errors. Moreover, by simulating the strategies on the worst-case scenarios where heat pumps are allocated to end-users at the end of the feeder, we determine the individual peak shaving strategy can slightly increase the heat pump hosting capacity from 49% where no control is imposed to 51%, while the central optimal power flow control allows 100% heat pump connections without causing grid congestion. Finally, recommendations to increase the heat pump hosting capacity are given based on simulation results