Mint views: Materialized in-network top-k views in sensor networks

In this paper we introduce MINT (materialized in-network top-k) Views, a novel framework for optimizing the execution of continuous monitoring queries in sensor networks. A typical materialized view V maintains the complete results of a query Q in order to minimize the cost of future query executions. In a sensor network context, maintaining consistency between V and the underlying and distributed base relation R is very expensive in terms of communication. Thus, our approach focuses on a subset V(sube. V) that unveils only the k highest-ranked answers at the sink for some user defined parameter k. We additionally provide an elaborate description of energy-conscious algorithms for constructing, pruning and maintaining such recursively- defined in-network views. Our trace-driven experimentation with real datasets show that MINT offers significant energy reductions compared to other predominant data acquisition models

Information Integration - the process of integration, evolution and versioning

At present, many information sources are available wherever you are. Most of the time, the information needed is spread across several of those information sources. Gathering this information is a tedious and time consuming job. Automating this process would assist the user in its task. Integration of the information sources provides a global information source with all information needed present. All of these information sources also change over time. With each change of the information source, the schema of this source can be changed as well. The data contained in the information source, however, cannot be changed every time, due to the huge amount of data that would have to be converted in order to conform to the most recent schema.\ud In this report we describe the current methods to information integration, evolution and versioning. We distinguish between integration of schemas and integration of the actual data. We also show some key issues when integrating XML data sources

Trade-offs in Static and Dynamic Evaluation of Hierarchical Queries

We investigate trade-offs in static and dynamic evaluation of hierarchical queries with arbitrary free variables. In the static setting, the trade-off is between the time to partially compute the query result and the delay needed to enumerate its tuples. In the dynamic setting, we additionally consider the time needed to update the query result in the presence of single-tuple inserts and deletes to the input database. Our approach observes the degree of values in the database and uses different computation and maintenance strategies for high-degree and low-degree values. For the latter it partially computes the result, while for the former it computes enough information to allow for on-the-fly enumeration. The main result of this work defines the preprocessing time, the update time, and the enumeration delay as functions of the light/heavy threshold and of the factorization width of the hierarchical query. By conveniently choosing this threshold, our approach can recover a number of prior results when restricted to hierarchical queries. For a restricted class of hierarchical queries, our approach can achieve worst-case optimal update time and enumeration delay conditioned on the Online Matrix-Vector Multiplication Conjecture.Comment: Technical Report; 52 pages. The updated version contains: new diagrams and plots summarizing known results and putting the results of the paper into context; introduction of delta_i-hieararchical queries, for any non-negative integer i; optimality results for delta_0- and delta_1-hieararchical querie

A New Rational Algorithm for View Updating in Relational Databases

The dynamics of belief and knowledge is one of the major components of any autonomous system that should be able to incorporate new pieces of information. In order to apply the rationality result of belief dynamics theory to various practical problems, it should be generalized in two respects: first it should allow a certain part of belief to be declared as immutable; and second, the belief state need not be deductively closed. Such a generalization of belief dynamics, referred to as base dynamics, is presented in this paper, along with the concept of a generalized revision algorithm for knowledge bases (Horn or Horn logic with stratified negation). We show that knowledge base dynamics has an interesting connection with kernel change via hitting set and abduction. In this paper, we show how techniques from disjunctive logic programming can be used for efficient (deductive) database updates. The key idea is to transform the given database together with the update request into a disjunctive (datalog) logic program and apply disjunctive techniques (such as minimal model reasoning) to solve the original update problem. The approach extends and integrates standard techniques for efficient query answering and integrity checking. The generation of a hitting set is carried out through a hyper tableaux calculus and magic set that is focused on the goal of minimality.Comment: arXiv admin note: substantial text overlap with arXiv:1301.515