6907477
Achieving Communication Efficiency through Push-Pull Partitioning of Semantic Spaces to Disseminate Dynamic Information
Many database applications that need to disseminate dynamic information from a server to various clients can suffer from heavy communication costs. Data caching at a client can help mitigate these costs, particularly when individual PUSH-PULL decisions are made for the different semantic regions in the data space. The server is responsible for notifying the client about updates in the PUSH regions. The client needs to contact the server for queries that ask for data in the PULL regions. We call the idea of partitioning the data space into PUSH-PULL regions to minimize communication cost data gerrymandering. In this paper, we present solutions to technical challenges in adopting this simple but powerful idea. We give a provably optimal-cost dynamic programming algorithm for gerrymandering on a single query attribute. We propose a family of efficient heuristics for gerrymandering on multiple query attributes. We handle the dynamic case in which the workloads of queries and updates evolve over time. We validate our methods through extensive experiments on real and synthetic data sets
1386080
Using Constraints to Describe Source Contents in Data Integration Systems
In data integration systems, information sources often have constraints, such as "all houses stored at a source have a unique address." These constraints are useful in computing answers to queries and facilitating information exchange.
2113121
Computing capabilities of mediators
Existing data-integration systems based on the mediation architecture employ a variety of mechanisms to describe the query-processing capabilities of sources. However, these systems do not compute the capabilities of the mediators based on the capabilities of the sources they integrate. In this paper, we proposed a framework to capture a rich variety of query-processing capabilities of data sources and mediators. We present algorithms to compute the set of supported queries of a mediator, based on the capability limitations of its sources. Our algorithms take into consideration a variety of query-processing techniques employed by mediators to enhance the set of supported queries.
7967297
2D BubbleUp: Managing Parallel Disks for Media Servers
In this study we present a scheme called two-dimensional BubbleUp (2DB) for managing parallel disks in a multimedia server. Its goal is to reduce initial latency for interactive multimedia applications, while balancing disk loads to maintain high throughput. The 2DB scheme consists of a data placement and a request scheduling policy. The data placement policy replicates frequently accessed data and places them cyclically throughout the disks. The request scheduling policy attempts to maintain free ``service slots'''' in the immediate future. These slots can then be used to quickly service newly arrived requests. Through examples and simulation, we show that our scheme significantly reduces initial latency and maintains throughput comparable to that of the traditional schemes.
8510271
Supporting Efficient Record Linkage for Large Data Sets Using Mapping Techniques
This paper describes an efficient approach to record linkage. Given two lists of records, the record-linkage problem consists of determining all pairs that are similar to each other, where the overall similarity between two records is defined based on domain-specific similarities over individual attributes. The record-linkage problem arises naturally in the context of data cleansing that usually precedes data analysis and mining. Since the scalability issue of record linkage was addressed in [21], the repertoire of database techniques dealing with multidimensional data sets has significantly increased. Specifically, many effective and efficient approaches for distance-preserving transforms and similarity joins have been developed. Based on these advances, we explore a novel approach to record linkage. For each attribute of records, we first map values to a multidimensional Euclidean space that preserves domain-specific similarity. Many mapping algorithms can be applied, and we use the Fastmap approach [16] as an example. Given the merging rule that defines when two records are similar based on their attribute-level similarities, a set of attributes are chosen along which the merge will proceed. A multidimensional similarity join over the chosen attributes is used to find similar pairs of records. Our extensive experiments using real data sets show that our solution has very good efficiency and recall.
10902397
Protecting Individual Information Against Inference Attacks in Data Publishing
In many data-publishing applications, the data owner needs to protect sensitive information pertaining to individuals. Meanwhile, certain information is required to be published. The sensitive information could be considered as leaked, if an adversary can infer the real value of a sensitive entry with a high confidence. In this paper we study how to protect sensitive data when an adversary can do inference attacks using association rules derived from the data. We formulate the inference attack model, and develop complexity results on computing a safe partial table. We classify the general problem into subcases based on the requirements of publishing information, and propose the corresponding algorithms for finding a safe partial table to publish. We have conducted an empirical study to evaluate these algorithms on real data.
8468902
Segmentation and recognition of multi-attribute motion sequences
In this work, we focus on fast and efficient recognition of motions in multi-attribute continuous motion sequences. 3D motion capture data, animation motion data, and sensor data from gesture sensing devices are examples of multi-attribute continuous motion sequences. These sequences have multiple attributes rather than only one attribute as time series data has. Motions can have different rates and durations, and the resulting data can thus have different lengths. Also, motion data can have noises due to transitions between successive motions. Hence, traditional distance measuring approaches used for time series data (such as Euclidean distances or dynamic time-warped distances) are not suitable for recognition in multi-attribute motion sequences. Hence, we have defined a similarity measure based on the analysis of singular value decomposition (SVD) properties of similar multi-attribute motions. A five-phase algorithm has then been proposed that gives good pruning power by exploiting the proximity of continuous motion data. We experimented this algorithm with data from different sources: 3D motion capture devices, animation motions, and CyberGlove gesture sensing device. These experiments show that our algorithm can segment and recognize long motion streams with high accuracy and in real time without knowing beforehand the number of motions in a stream.
19589601
Single machine scheduling to minimize total compression plus weighted flow cost is NP-hard
Abstract This article analyzes the computational complexity of a single machine scheduling problem to minimize total compression plus weighted flow cost. Vickson [Oper. Res. 28 (1980) 1155–1167] studied this problem and conjectured that it was NP-hard. The complexity of this problem remained open since then. We give a positive answer to this conjecture, showing that this problem is NP-hard.
10480954
Isomorphisms of Finite Cayley Digraphs of Bounded Valency
LetGbe a finite group,Sa subset ofG\{1}, and let Cay(G,S) denote the Cayley digraph ofGwith respect toS. If, for all subsetsS,TofG\{1} of size at mostm, Cay(G,S)?Cay(G,T) implies thatS?=Tfor some??Aut(G), thenGis called anm-DCI-group. In this paper, we prove that, form?2, allm-DCI-groups are of the formU×V, where (|U|, |V|)=1,Uis abelian andVbelongs to an explicitly determined list of groups. Moreover Sylow subgroups of such groups satisfy some very restrictive conditions.
5288493
Indexing of variable length multi-attribute motion data
Haptic data such as 3D motion capture data and sign language animation data are new forms of multimedia data. The motion data is multi-attribute, and indexing of multi-attribute data is important for quickly pruning the majority of irrelevant motions in order to have real-time animation applications. Indexing of multi-attribute data has been attempted for data of a few attributes by using R-tree or its variants after dimensionality reduction. In this paper, we exploit the singular value decomposition (SVD) properties of multi-attribute motion data matrices to obtain one representative vector for each of the motion data matrices of dozens or hundreds of attributes. Based on this representative vector, we propose a simple and efficient interval-tree based index structure for indexing motion data with large amount of attributes. At each tree level, only one component of the query vector needs to be checked during searching, comparing to all the components of the query vector that should get involved if an R-tree or its variants are used for indexing. Searching time is independent of the number of pattern motions indexed by the tree, making the index structure well scalable to large data repositories. Experiments show that up to 91∼93% irrelevant motions can be pruned for a query with no false dismissals, and the query searching time is less than 30 μ <i>s</i> with the existence of motion variations.
3458490
Supporting ranking and clustering as generalized order-by and group-by
The Boolean semantics of SQL queries cannot adequately capture the "fuzzy" preferences and "soft" criteria required in non-traditional data retrieval applications. One way to solve this problem is to add a flavor of "information retrieval" into database queries by allowing fuzzy query conditions and flexibly supporting grouping and ranking of the query results within the DBMS engine. While ranking is already supported by all major commercial DBMSs natively, support of flexibly grouping is still very limited (i.e., group-by).
 In this paper, we propose to generalize group-by to enable flexible grouping (clustering specifically) of the query results. Different from clustering in data mining applications, our focus is on supporting efficient clustering of Boolean results generated at query time. Moreover, we propose to integrate ranking and clustering with Boolean conditions, forming a new type of ClusterRank query to allow structured data retrieval. Such an integration is nontrivial in terms of both semantics and query processing. We investigate various semantics of this type of queries. To process such queries, a straightforward approach is to simply glue the techniques developed for ranking-only and clustering-only together. This approach is costly since both ranking and clustering are treated as blocking post-processing tasks upon Boolean query results by existing techniques. We propose a summary-based evaluation method that utilizes bitmap index to seamlessly integrate Boolean conditions, clustering, and ranking. Experimental study shows that our approach significantly outperforms the straightforward one and maintains high clustering quality.