1,721,002 research outputs found
PrefetchGuide: Capturing Navigational Access Patterns for Prefetching in Client/Server Object-Oriented/Object-Relational DBMSs
No full textIn prefetching, the objects that are expected to be accessed in the future are fetched from the server to the client in advance. Prefetching reduces the number of round-trips and increases the system performance. To prefetch object effectively, we need to correctly predict the future navigational patterns. In this paper, we propose the PrefetchGuide, a novel data structure that captures the navigational access patterns. We also formally define the notion of the attribute access log set and analyze the navigational access patterns that can be captured by the PrefetchGuide. We then present an prefetching algorithm using the PrefetchGuide. To show effectiveness of our algorithm, we have conducted extensive experiments in a prototype object-relational database management systems (DBMS). The results show that our method significantly outperforms the state-of-the-art prefetching method. These results indicate that our approach provides a practical method that can be implemented in commercial object-oriented/object-relationaI DBMSs. We believe our method is practically usable for object-oriented programmers and DBMS implementors. (C) 2002 Elsevier Science Inc. All rights reserved.X111014sciescopu
Towards intelligent in-vehicle sensor database management systems
No full textDue to the evolution of technologies for sensor and wireless communication, there has been increasing attention to research on an intelligent vehicle that supports safe driving by exploiting large traffic data gathered from traffic environments such as vehicles and road side units, as well as data gathered from sensors mounted on the vehicle. In this paper, we study an in-vehicle sensor database management system (DBMS). In the proposed approach, simply called in-vehicle DBMS approach, DBMS inside the ego-vehicle manages, gathers, and processes traffic and sensor data onboard such as signal data and multimedia data including map and image data. We classify the requirements of applications using the in-vehicle DBMS into data modeling and query processing. We also propose a system architecture for an in-vehicle DBMS which solves those issues and discuss database techniques offered by the system.1110sciescopu
Developing a Hybrid Dictionary-based Bio-entity Recognition Technique
Full text linkBackground: Bio-entity extraction is a pivotal component for information extraction from biomedical literature. The dictionary-based bio-entity extraction is the first generation of Named Entity Recognition (NER) techniques. Methods: This paper presents a hybrid dictionary-based bio-entity extraction technique. The approach expands the bio-entity dictionary by combining different data sources and improves the recall rate through the shortest path edit distance algorithm. In addition, the proposed technique adopts text mining techniques in the merging stage of similar entities such as Part of Speech (POS) expansion, stemming, and the exploitation of the contextual cues to further improve the performance. Results: The experimental results show that the proposed technique achieves the best or at least equivalent performance among compared techniques, GENIA, MESH, UMLS, and combinations of these three resources in F-measure. Conclusions: The results imply that the performance of dictionary-based extraction techniques is largely influenced by information resources used to build the dictionary. In addition, the edit distance algorithm shows steady performance with three different dictionaries in precision whereas the context-only technique achieves a high-end performance with three difference dictionaries in recall.X1133Ysciescopu
PrefetchGuide: capturing navigational access patterns for prefetching in client/server object-oriented/object-relational DBMSs
No full textIn prefetching, the objects that are expected to be accessed in the future are fetched from the server to the client in advance. Prefetching reduces the number of round-trips and increases the system performance. To prefetch object effectively, we need to correctly predict the future navigational patterns. In this paper, we propose the PrefetchGuide, a novel data structure that captures the navigational access patterns. We also formally define the notion of the attribute access log set and analyze the navigational access patterns that can be captured by the PrefetchGuide. We then present an prefetching algorithm using the PrefetchGuide. To show effectiveness of our algorithm, we have conducted extensive experiments in a prototype object-relational database management systems (DBMS). The results show that our method significantly outperforms the state-of-the-art prefetching method. These results indicate that our approach provides a practical method that can be implemented in commercial object-oriented/object-relationaI DBMSs. We believe our method is practically usable for object-oriented programmers and DBMS implementors. (C) 2002 Elsevier Science Inc. All rights reserved
Transformation-Based Spatial Partition Join
No full textSpatial joins find all pairs of spatial objects that satisfy a given spatial relationship. In this paper, we present the Transformation-Based Spatial Partition Join algorithm (TSPI), a new spatial join algorithm that performs join in the transform space without using indexes. Since the existing algorithms deal with extents of spatial objects in the original space, they either need to replicate the spatial objects or have a relatively complex partition structure - resulting in degrading performance. In contrast, the Transformation-Based Spatial Partition join transforms objects in the original space into points in the transform space and deals only with points having no extents. The transformation does not incur any additional overhead. Thus, Our algorithm has advantages over existing ones in that (1) it obviates the need for replicating spatial objects, and (2) its partition structure is simple. As a result, it always has better performance compared to existing algorithms. Extensive experiments show that the Transformation-Rased Spatial Partition. join improves performance by 19.4-38.0% over the existing algorithms compared
Dynamic buffer allocation in video-on-demand systems
No full textIn video-on-demand (VOD) systems, as the size of the buffer allocated to user requests increases, initial latency and memory requirements increase. Hence, the buffer size must be minimized. The existing static buffer allocation scheme, however, determines the buffer size based on the assumption that the system is in the fully loaded state. Thus, when the system is in a partially loaded state, the scheme allocates a buffer larger than necessary to a user request. This paper proposes a dynamic buffer allocation scheme that allocates to user requests buffers of the minimum size in a partially loaded state, as well as in the fully loaded state. The inherent difficulty in determining the buffer size in the dynamic buffer allocation scheme is that the size of the buffer currently being allocated is dependent on the number of and the sizes of the buffers to be allocated in the next service period. We solve this problem by the predict, and-enforce strategy, where we predict the number and the sizes of future buffers based on inertia assumptions and enforce these assumptions at runtime. Any violation of these assumptions is resolved by deferring service to the violating new user request until the assumptions are satisfied. Since the,size of the current buffer is dependent on the sizes of the future buffers, it is represented by a recurrence equation. We provide a solution to this equation, which can be computed at the system initialization time for runtime efficiency. We have performed extensive analysis and simulation. The results show that the dynamic buffer allocation scheme reduces initial latency (averaged over the number of user requests in service from one to the maximum capacity) to (29.4)/(1) similar to (11.0)/(1) of that for the static one and, by reducing the memory requirement, increases the number of concurrent user requests to 2.36 similar to 3.25 times that of the static one when averaged over the amount of system memory available. These results demonstrate that the dynamic buffer allocation scheme significantly improves the performance and capacity of VOD systems.X1189sciescopu
A tunable class hierarchy index for object-oriented databases using a multidimensional index structure
No full textX110sciescopu
Design and control of a disk-type integrated motor-bearing system
No full textA disk-type integrated motor-bearing system having axial magnetic flux is newly invented and its design, analysis, and control methods are presented. Sinusoidal motoring currents to four symmetrically placed winding groups produce a torque, whereas control currents of the same magnitude but opposite signs added to the opposite winding groups create radial forces. The control currents are intended to break force symmetry, resulting in unbalanced radial forces. The system employs two stators not only to effectively remove the rotational frequency modulation effect in the radial control forces, but also to reduce the torque ripple. It is shown that the prototype integrated motor-bearing system built in the laboratory succeeds in stable radial direction control and operation of the rotor
DSP-CC: I/O Efficient Parallel Computation of Connected Components in Billion-scale Networks
No full textComputing connected components is a core operation on graph data. Since billion-scale graphs cannot be resident in memory of a single server, several approaches based on distributed machines have recently been proposed. The representative methods are Hash-To-Min and PowerGraph. Hash-To-Min is the state-of-the art disk-based distributed method which minimizes the number of MapReduce rounds. PowerGraph is the-state-of-the-art in-memory distributed system, which is typically faster than the disk-based distributed one, however, requires a lot of machines for handling billion-scale graphs. In this paper, we propose an I/O efficient parallel algorithm for billion-scale graphs in a single PC. We first propose the Disk-based Sequential access-oriented Parallel processing (DSP) model that exploits sequential disk access in terms of disk I/Os and parallel processing in terms of computation. We then propose an ultra-fast disk-based parallel algorithm for computing connected components, DSP-CC, which largely improves the performance through sequential disk scan and page-level cache-conscious parallel processing. Extensive experimental results show that DSP-CC 1) computes connected components in billion-scale graphs using the limited memory size whereas in-memory algorithms can only support medium-sized graphs with the same memory size, and 2) significantly outperforms all distributed competitors as well as a representative disk-based parallel method.111Nsciescopu
The G* graph database: efficiently managing large distributed dynamic graphs
No full textFrom sensor networks to transportation infrastructure to social networks, we are awash in data. Many of these real-world networks tend to be large ("big data") and dynamic, evolving over time. Their evolution can be modeled as a series of graphs. Traditional systems that store and analyze one graph at a time cannot effectively handle the complexity and subtlety inherent in dynamic graphs. Modern analytics require systems capable of storing and processing series of graphs. We present such a system. G* compresses dynamic graph data based on commonalities among the graphs in the series for deduplicated storage on multiple servers. In addition to the obvious space-saving advantage, large-scale graph processing tends to be I/O bound, so faster reads from and writes to stable storage enable faster results. Unlike traditional database and graph processing systems, G* executes complex queries on large graphs using distributed operators to process graph data in parallel. It speeds up queries on multiple graphs by processing graph commonalities only once and sharing the results across relevant graphs. This architecture not only provides scalability, but since G* is not limited to processing only what is available in RAM, its analysis capabilities are far greater than other systems which are limited to what they can hold in memory. This paper presents G*'s design and implementation principles along with evaluation results that document its unique benefits over traditional graph processing systems.111412sciescopu
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