An O(1) Solution to the Prefix Sum Problem on a Specialized Memory Architecture

In this paper we study the Prefix Sum problem introduced by Fredman. We show that it is possible to perform both update and retrieval in O(1) time simultaneously under a memory model in which individual bits may be shared by several words. We also show that two variants (generalizations) of the problem can be solved optimally in $\Theta(\lg N)$ time under the comparison based model of computation.Comment: 12 page

An O(1) solution to the prefix sum problem on a specialized memory architecture

In this paper we study the Prefix Sum problem introduced by Fredman. We show that it is possible to perform both update and retrieval in O(1) time simultaneously under a memory model in which individual bits may be shared by several words. We also show that two variants (generalizations) of the problem can be solved optimally in Θ (lgN) time under the comparison based model of computation.4th IFIP International Conference on Theoretical Computer ScienceRed de Universidades con Carreras en Informática (RedUNCI

Consistent data aggregate retrieval for sensor network systems.

Lee Lok Hang.Thesis (M.Phil.)--Chinese University of Hong Kong, 2005.Includes bibliographical references (leaves 87-93).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivChapter 1 --- Introduction --- p.1Chapter 1.1 --- Sensors and Sensor Networks --- p.3Chapter 1.2 --- Sensor Network Deployment --- p.7Chapter 1.3 --- Motivations --- p.7Chapter 1.4 --- Contributions --- p.9Chapter 1.5 --- Thesis Organization --- p.10Chapter 2 --- Literature Review --- p.11Chapter 2.1 --- Data Cube --- p.11Chapter 2.2 --- Data Aggregation in Sensor Networks --- p.12Chapter 2.2.1 --- Hierarchical Data Aggregation --- p.13Chapter 2.2.2 --- Gossip-based Aggregation --- p.13Chapter 2.2.3 --- Hierarchical Gossip Aggregation --- p.13Chapter 2.3 --- GAF Algorithm --- p.14Chapter 2.4 --- Concurrency Control --- p.17Chapter 2.4.1 --- Two-phase Locking --- p.17Chapter 2.4.2 --- Timestamp Ordering --- p.18Chapter 3 --- Building Distributed Data Cubes in Sensor Network --- p.20Chapter 3.1 --- Aggregation Operators --- p.21Chapter 3.2 --- Distributed Prefix (PS) Sum Data Cube --- p.22Chapter 3.2.1 --- Prefix Sum (PS) Data Cube --- p.22Chapter 3.2.2 --- Notations --- p.24Chapter 3.2.3 --- Querying a PS Data Cube --- p.25Chapter 3.2.4 --- Building Distributed PS Data Cube --- p.27Chapter 3.2.5 --- Time Bounds --- p.32Chapter 3.2.6 --- Fast Aggregate Queries on Multiple Regions --- p.37Chapter 3.2.7 --- Simulation Results --- p.43Chapter 3.3 --- Distributed Local Prefix Sum (LPS) Data Cube --- p.50Chapter 3.3.1 --- Local Prefix Sum Data Cube --- p.52Chapter 3.3.2 --- Notations --- p.55Chapter 3.3.3 --- Querying an LPS Data Cube --- p.56Chapter 3.3.4 --- Building Distributed LPS Data Cube --- p.61Chapter 3.3.5 --- Time Bounds --- p.63Chapter 3.3.6 --- Fast Aggregate Queries on Multiple Regions --- p.67Chapter 3.3.7 --- Simulation Results --- p.68Chapter 3.3.8 --- Distributed PS Data Cube Vs Distributed LPS Data Cube --- p.74Chapter 4 --- Concurrency Control and Consistency in Sensor Networks --- p.76Chapter 4.1 --- Data Inconsistency in Sensor Networks --- p.76Chapter 4.2 --- Traditional Concurrency Control Protocols and Sensor Networks --- p.80Chapter 4.3 --- The Consistent Retrieval of Data from Distributed Data Cubes --- p.81Chapter 5 --- Conclusions --- p.85References --- p.87Appendix --- p.94A Publications --- p.9