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Dyreson and Snodgrass as well as Dekhtyar et. al. have provided a probabilistic model (as well as compelling example applications) for why there may be temporal indeterminacy in databases. In this paper, we first propose a formal model for aggregate computation in such databases when there is uncertainty not just in the temporal attribute, but also in the ordinary (non-temporal) attributes. We identify two types of aggregates: event correlated aggregates, and non event correlated aggregations, and provide efficient algorithms for both of them. We prove that our algorithms are correct, and we present experimental results showing that the algorithms work well in practice.

So far, we have provided three illustrative examples: constraint problem solving, distributed search, and spatial learning. We have stated the basic problem requirements and showed the ideas behind the AOC solutions, ranging from their formulations to the emergence of collective solutions through self-organization.

From the illustrations, we can note that using an AOC-based method to solve a problem is essentially to build an autonomous system, which usually involves a group of autonomous entities residing in an environment. Entities are equipped with some simple behaviors, such as move, diffuse, breed, and decay, and one or more goals (e.g., to locate a pixel in a homogeneous region). In order to achieve their goals, entities either directly interact with each other or indirectly interact via their environment. Through interactions, entities accumulate their behavioral outcomes and some collective behaviors or patterns emerge. Ideally, these collective behaviors or patterns are what we are expecting, i.e., solutions to our problems at hand.

In this chapter, we have identified and discussed several important issues in designing and engineering an AOC system. Generally speaking, an autonomous entity in an AOC system contains several common functional modules for sensing information, making decisions, and executing actions. Three phases are involved in developing AOC systems for achieving various objectives. The engineering steps in these phases can differ from one system to another in terms of human involvement, depending on our knowledge about problems or systems at hand. Moreover, we have identified several key features and attributes in an engineered AOC system, such as locality and amplification. We have discussed some important performance characteristics as well as determining factors in its performance.

For an AOC system, autonomous entities and an environment are its key elements. Interactions between entities and their environment are the force that drives an AOC system to evolve towards certain desired states. Self-organization is the essential process of its working mechanism. 1n this chapter, we have formally defined the above notions as well as their intentions, and have provided a general framework for an AOC system. Based on the framework, we know how to build an AOC system, given a problem to be solved or a complex system to be modeled. Specifically, we have better knowledge of:

Dyreson and Snodgrass as well as Dekhtyar et. al. have provided a probabilistic model (as well as compelling example applications) for why there may be temporal indeterminacy in databases. In this paper, we first propose a formal model for aggregate computation in such databases when there is uncertainty not just in the temporal attribute, but also in the ordinary (non-temporal) attributes. We identify two types of aggregates: event correlated aggregates, and non event correlated aggregations, and provide efficient algorithms for both of them. We prove that our algorithms are correct, and we present experimental results showing that the algorithms work well in practice.

Dyreson and Snodgrass as well as Dekhtyar et. al. have provided a probabilistic model (as well as compelling example applications) for why there may be temporal indeterminacy in databases. In this paper, we first propose a formal model for aggregate computation in such databases when there is uncertainty not just in the temporal attribute, but also in the ordinary (non-temporal) attributes. We identify two types of aggregates: event correlated aggregates, and non event correlated aggregations, and provide efficient algorithms for both of them. We prove that our algorithms are correct, and we present experimental results showing that the algorithms work well in practice.

Dyreson and Snodgrass as well as Dekhtyar et. al. have provided a probabilistic model (as well as compelling example applications) for why there may be temporal indeterminacy in databases. In this paper, we first propose a formal model for aggregate computation in such databases when there is uncertainty not just in the temporal attribute, but also in the ordinary (non-temporal) attributes. We identify two types of aggregates: event correlated aggregates, and non event correlated aggregations, and provide efficient algorithms for both of them. We prove that our algorithms are correct, and we present experimental results showing that the algorithms work well in practice.

Dyreson and Snodgrass as well as Dekhtyar et. al. have provided a probabilistic model (as well as compelling example applications) for why there may be temporal indeterminacy in databases. In this paper, we first propose a formal model for aggregate computation in such databases when there is uncertainty not just in the temporal attribute, but also in the ordinary (non-temporal) attributes. We identify two types of aggregates: event correlated aggregates, and non event correlated aggregations, and provide efficient algorithms for both of them. We prove that our algorithms are correct, and we present experimental results showing that the algorithms work well in practice.