2682993
Monotonic aggregation in deductive databases
We propose a semantics for aggregates in deductive databases based on a notion of minimality. Unlike some previous approaches, we form a minimal model of a program component including aggregate operators, rather than insisting that the aggregate apply to atoms that have been fully determined, or that aggregate functions are rewritten in terms of negation. In order to guarantee the existence of such a minimal model we need to insist that the domains over which we are aggregating are complete lattices, and that the program is in a sense monotonic. Our approach generalizes previous approaches based on the well-founded semantics and various forms of stratification. We are also able to handle a large variety of monotonic (or pseudo-monotonic) aggregate functions.
38852147
On Negation in HiLog
Abstract The logic HiLog of Chen, Kifer, and Warren has a second order syntax, while its semantics is first order. HiLog programs with negative literals in the body are considered. A stable-model semantics and a well-founded semantics for this class of program are defined, and it is shown that these semantics generalize the stable-model semantics and the well-founded semantics, respectively, for range-restricted normal programs. A second order property called preservation under extensions is proposed and investigated. Preservation under extensions ensures that the semantics of a program is not changed when rules having no symbols in common with the program are appended to the program. It is shown that for normal programs, domain independence and preservation under extensions are equivalent, while for HiLog programs, preservation under extensions is strictly stronger. Range restrictedness is generalized to HiLog programs in two ways, and it is shown that range restricted HiLog programs are preserved under extensions with respect to the well-founded semantics. Conditions under which the well-founded semantics is two-valued are investigated, and the class of modularly stratified programs is generalized to HiLog. An extension of magic-set techniques to modularly stratified HiLog programs is described.
31185043
Inferring negative information from disjunctive databases
We propose criteria that any rule for inferring negative information from disjunctive databases should satisfy, and examine existing rules from this viewpoint. We then present a new inference rule, the ‘disjunctive database rule’ (DDR), and compare it to the existing rules with respect to the criteria. In particular, the DDR is equivalent to the CWA for definite databases, it infers no more negative information than the GCWA, and it interprets disjunction inclusively rather than exclusively. We generalize the DDR to a class of layered databases, describe an implementation of the DDR, ‘negation as positive failure’, and study its soundness and completeness properties.
9401708
Complex Aggregation at Multiple Granularities
Datacube queries compute simple aggregates at multiple granularities. In this paper we examine the more general and useful problem of computing a complex subquery involving multiple dependent aggregates at multiple granularities. We call such queries multi-feature cubes. An example is Broken down by all combinations of month and customer, find the fraction of the total sales in 1996 of a particular item due to suppliers supplying within 10% of the minimum price (within the group), showing all subtotals across each dimension. We classify multi-feature cubes based on the extent to which fine granularity results can be used to compute coarse granularity results; this classification includes distributive, algebraic and holistic multi-feature cubes. We provide syntactic sufficient conditions to determine when a multi-feature cube is either distributive or algebraic. This distinction is important because, as we show, existing datacube evaluation algorithms can be used to compute multi-feature cubes that are distributive or algebraic, without any increase in I/O complexity. We evaluate the CPU performance of computing multi-feature cubes using the datacube evaluation algorithm of Ross and Srivastava. Using a variety of synthetic, benchmark and real-world data sets, we demonstrate that the CPU cost of evaluating distributive multi-feature cubes is comparable to that of evaluating simple datacubes. We also show that a variety of holistic multi-feature cubes can be evaluated with a manageable overhead compared to the distributive case.
16656102
Groupwise Processing of Relational Queries
In this paper, we define and examine a particular class of queries called group queries. Group queries are natural queries in many decisionsupport applications. The main characteristic of a group query is that it can be executed in a groupby-group fashion. In other words, the underlying relation(s) can be partitioned (based on some set of attributes) into disjoint groups, and each group can be processed separately. We give a syntactic criterion to identify these queries and prove its sufficiency. We also prove the strong result that every group query has an equivalent formulation that satisfies our syntactic criterion. We describe a general evaluation technique for group queries, and demonstrate how an optimizer can determine this plan. We then consider more complex queries whose components are group queries with potentially different partitioning attributes. We give two methods to identify group query components within such a query. We also give some performance results for group queries expressed in standard SQL, comparing a commercial database system with our optimized plan on top of the same commercial system. These results indicate that there are significant potential performance improvements.
7567374
Foundations of Aggregation Constraints
We introduce a new constraint domain, aggregation constraints, which is useful in database query languages, and in constraint logic programming languages that incorporate aggregate functions. We study the fundamental problem of checking if a conjunction of aggregation constraints is solvable, and present undecidability results for many different classes of aggregation constraints. We describe a complete and minimal axiomatization of the class of aggregation constraints over finite multisets of reals, which permits a natural reduction from the class of aggregation constraints to the class of mixed integer/real, non-linear arithmetic constraints. We then present a polynomial-time algorithm that directly checks for solvability of a useful class of aggregation constraints, where the reduction-based approach does not lead to efficient checks for solvability.
7149127
Optimal multiplexing of heterogeneous traffic with hard constraint
Considered are optimal dynamic policies for multiplexing &Kgr; + 1 heterogeneous traffic types onto a single communication channel. The packet types arrive to the channel according to independent Poisson processes. The service requirements are exponential with type dependent means. The optimization criterion is to minimize a linear combination of the average delays for packet types 1 to &Kgr;, while simultaneously subjecting the average delay of type-0 packets to a hard constraint. The optimal multiplexing policy is shown to be a randomized modification of the “μc rule”. The optimization problem is thereby reduced to a problem of finding the optimal randomization factor; an algorithm, which can be implemented in real time, is given to do this for two particular cases.
22940589
Asynchronous voice: a personal account
Asynchronous voice is the interactive communication process of people leaving voice messages for other people and the other people responding with their voice messages. A primitive form of asynchronous voice is a kind of telephone tag in which people use voice mail to have an interactive conversation. The author gives a personal account of his work with asynchronous voice and asynchronous learning.
6259994
Hierarchical Peer-To-Peer Systems
Structured peer-to-peer (P2P) lookup services organize peers into a flat overlay network and offer distributed hash table (DHT) functionality. Data is associated with keys and each peer is responsible for a subset of the keys. In hierarchical DHTs, peers are organized into groups, and each group has its autonomous intra-group overlay network and lookup service. Groups are organized in a top-level overlay network. To find a peer that is responsible for a key, the top-level overlay first determines the group responsible for the key; the responsible group then uses its intra-group overlay to determine the specific peer that is responsible for the key. We provide a general framework for hierarchical DHTs with scalable overlay management. We specifically study a two-tier hierarchy that uses Chord for the top level. Our analysis shows that by using the most reliable peers in the top level, the hierarchical design significantly reduces the expected number of hops. We also present a method to construct hierarchical DHTs that map well to the Internet topology and achieve short intra-group communication delay. The results demonstrate the feasibility of locality-based peer groups, which allow P2P systems to take full advantage of the hierarchical design.
11677427
Stochastic fluid models for cache clusters
Clusters of Web caches are extensively used by different types of organizations, including companies, universities, ISPs, and CDNs. To model Web caches, we must account for two types of stochastic events: objects being pulled into/out of the cache cluster at random times, and caches going up and down at random times. Detailed stochastic models of such complex systems quickly become intractable. In this paper we propose a stochastic fluid model which captures the salient characteristics of a cache cluster. The stochastic fluid model replaces the object arrivals to the cluster and departures (object modification/expiration) with a fluid flow, but maintains the up/down dynamics of the original system. The model can be applied to a variety of cluster routing policies, and provides a simple means to estimate the hit rate. We compare the results of the stochastic fluid model with that of a simulation of the real system. We find the fluid model to not only be a close approximation, but also to exhibit the key qualitative properties of the original system. We conclude that stochastic fluid models show great potential in modeling a variety of content distribution systems.
9803668
Distributing layered encoded video through caches
The efficient distribution of stored information has become a major concern in the Internet which has increasingly become a vehicle for the transport of stored video. Because of the highly heterogeneous access to the Internet, researchers and engineers have argued for layered encoded video. We investigate delivering layered encoded video using caches. Based on a stochastic knapsack model we develop a model for the layered video caching problem. We propose heuristics to determine which videos and which layers in the videos should be cached. We evaluate the performance of our heuristics through extensive numerical experiments. We also consider two intuitive extensions to the initial problem.