Querical Data Networks

IntroductIon Recently, a family of massive self-organizing data networks has emerged. These networks mainly serve as large-scale distributed query processing systems. We term these networks Querical Data Networks (QDN). A QDN is a federation of a dynamic set of peer, autonomous nodes communicating through a transient-form intercon-nection. Data is naturally distributed among the QDN nodes in extra-fine grain, where a few data items are dynamically created, collected, and/or stored at each node. Therefore, the network scales linearly to the size of the dataset. With a dynamic dataset, a dynamic and large set of nodes, and a transient-form communication infrastructure, QDNs should be considered as the new generation of distributed database systems with significantly less constraining assumptions as compared to their ancestors. Peer-to-peer networks (Daswani, 2003) and sensor networks (Estrin, 1999, Akyildiz, 2002) are well-known examples of QDN. QDNs can be categorized as instances of " complex systems " (Bar-Yam, 1997) and studied using the complex system theory. Complex systems are (mostly natural) systems hard (or Querical Data Networks complex) to describe information-theoretically, and hard to analyze computationally. QDNs share the same characteristics with complex systems, and particularly, bear a significant similarity to a dominating subset of complex systems most properly modeled as large-scale interconnection of functionally similar (or peer) entities. The links in the model represent some kind of system specific entity-to-entity interaction. Social networks, a network of interacting people, and cellular networks, a network of interacting cells, are two instances of such complex systems. With these systems, complex global system behavior (e.g., a social revolution in a society, or food digestion in stomach!) is an emergent phenomenon, emerging from simple local interactions. Various fields of study, such as sociology, physics, biology, chemistry, etc., were founded to study different types of initially simple systems and have been gradually matured to analyze and describe instances of incrementally more complex systems. An interdisciplinary field of study, the complex system theory a , is recently founded based on the observation that analytical and experimental concepts, tools, techniques, and models developed to study an instance of complex system at one field can be adopted, often almost unchanged, to study other complex systems in other fields of study. More importantly, the complex system theory can be considered as a unifying meta-theory that explains common characteristics of complex systems. One can extend application of the complex system theory to QDNs by: 1. Adopting models and techniques …