The widespread distribution and availability of small-scale sensors, actuators, and embedded processors is transforming the physical world into a computing platform. One such example is a sensor network consisting of a large number of sensor nodes that combine physical sensing capabilities such as temperature, light, or seismic sensors with networking and computation capabilities. Applications range from environmental control, warehouse inventory, and health care to military environments. Existing sensor networks assume that the sensors are preprogrammed and send data to a central frontend where the data is aggregated and stored for offline querying and analysis. This approach has two major drawbacks. First, the user cannot change the behavior of the system on the fly. Second, conservation of battery power is a major design factor, but a central system cannot make use of in-network programming, which trades costly communication for cheap local computation.In this paper, we introduce the Cougar approach to tasking sensor networks through declarative queries. Given a user query, a query optimizer generates an efficient query plan for in-network query processing, which can vastly reduce resource usage and thus extend the lifetime of a sensor network. In addition, since queries are asked in a declarative language, the user is shielded from the physical characteristics of the network. We give a short overview of sensor networks, propose a natural architecture for a data management system for sensor networks, and describe open research problems in this area.

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The cougar approach to in-network query processing in sensor networks

Database management systems will continue to manage large data volumes. Thus, efficient algorithms for accessing and manipulating large sets and sequences will be required to provide acceptable performance. The advent of object-oriented and extensible database systems will not solve this problem. On the contrary, modern data models exacerbate the problem: In order to manipulate large sets of complex objects as efficiently as today's database systems manipulate simple records, query-processing algorithms and software will become more complex, and a solid understanding of algorithm and architectural issues is essential for the designer of database management software. This survey provides a foundation for the design and implementation of query execution facilities in new database management systems. It describes a wide array of practical query evaluation techniques for both relational and postrelational database systems, including iterative execution of complex query evaluation plans, the duality of sort- and hash-based set-matching algorithms, types of parallel query execution and their implementation, and special operators for emerging database application domains.

/pdf/query-evaluation-techniques-for-large-databases-45td1anbls.pdf

Query evaluation techniques for large databases

We survey the research on interactive evolutionary computation (IEC). The IEC is an EC that optimizes systems based on subjective human evaluation. The definition and features of the IEC are first described and then followed by an overview of the IEC research. The overview primarily consists of application research and interface research. In this survey the IEC application fields include graphic arts and animation, 3D computer graphics lighting, music, editorial design, industrial design, facial image generation, speed processing and synthesis, hearing aid fitting, virtual reality, media database retrieval, data mining, image processing, control and robotics, food industry, geophysics, education, entertainment, social system, and so on. The interface research to reduce human fatigue is also included. Finally, we discuss the IEC from the point of the future research direction of computational intelligence. This paper features a survey of about 250 IEC research papers.

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Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation

Until recently, the management of large image databases has relied exclusively on manually entered alphanumeric annotations. Systems are beginning to emerge in both the research and commercial sectors based on 'content-based' image retrieval, a technique which explicitly manages image assets by directly representing their visual attributes. The Virage image search engine provides an open framework for building such systems. The Virage engine expresses visual features as image 'primitives.' Primitives can be very general (such as color, shape, or texture) or quite domain specific (face recognition, cancer cell detection, etc.). The basic philosophy underlying this architecture is a transformation from the data-rich representation of explicit image pixels to a compact, semantic-rich representation of visually salient characteristics. In practice, the design of such primitives is non-trivial, and is driven by a number of conflicting real-world constraints (e.g. computation time vs. accuracy). The virage engine provides an open framework for developers to 'plug-in' primitives to solve specific image management problems. The architecture has been designed to support both static images and video in a unified paradigm. The infrastructure provided by the Virage engine can be utilized to address high-level problems as well, such as automatic, unsupervised keyword assignment, or image classification.

Virage image search engine: an open framework for image management

We present a method for searching in an image database using a query image that is similar to the intended target. The query image may be a hand-drawn sketch or a (potentially low-quality) scan of the image to be retrieved. Our searching algorithm makes use of multiresolution wavelet decompositions of the query and database images. The coefficients of these decompositions are distilled into small “signatures” for each image. We introduce an “image querying metric” that operates on these signatures. This metric essentially compares how many significant wavelet coefficients the query has in common with potential targets. The metric includes parameters that can be tuned, using a statistical analysis, to accommodate the kinds of image distortions found in different types of image queries. The resulting algorithm is simple, requires very little storage overhead for the database of signatures, and is fast enough to be performed on a database of 20,000 images at interactive rates (on standard desktop machines) as a query is sketched. Our experiments with hundreds of queries in databases of 1000 and 20,000 images show dramatic improvement, in both speed and success rate, over using a conventional L1, L2, or color histogram norm. CR

/pdf/fast-multiresolution-image-querying-2orn1468y6.pdf

Fast multiresolution image querying

The crystal structure of Bi2-xPbxSr2Co2Oy has been investigated by X-ray diffraction and electron diffraction analyses. In contrast to what has previously been considered a Bi2Sr2CaCu2O8-type structure, we found that this material has a misfit layer structure: two subcells with different periodicities are alternately stacked. We have studied the x dependence of the crystal structure and found a discontinuous phase transition at x 0.2, where only Bi-Sr-O layers show a drastic change in lattice constants. Pb doping induces metallic behavior, but the effect of the structural phase transition is less clearly observed, which indicates that the electronic properties are mainly dominated by CoO2 layers.

https://iopscience.iop.org/article/10.1143/JJAP.39.L747/pdf

Structural Phase Transition and Metallic Behavior in Misfit Layered (Bi, Pb)-Sr-Co-O System

1 Introduction In this paper we propose a new hash-partitioned join method using a dynamic de&aging strategy for large scale databases. The traditional hash-partitioned join methods such as the Hybrid Hash Join Method assume that the size of each bucket can be controlled by selecting a split function, and the characteristics of the buckets are statically specified. For materializing this assumption, we have to collect information about distributions of the join attribute value before processing a job. In general , however, join operations are applied to relations in which some restrictions are applied, and so it is not easy to collect that information before processing. If we cannot collect the information, the tuple distributions of each bucket may differ from the estimation. An example shows that the processing time in such a case becomes 1.4 times worse than the ideal one. In this paper we propose a strategy in which the destaging buckets are selected dynamically, instead of a static decision of them during the split phase. Using this strategy, we don't have to collect information before processing and this method can be applied in many cases, which are unsuited to traditional methods. When we apply this method to that example which we mentioned, we can get the same performance as the ideal one. Permission to copy without fee d ar pIlt of this matdd is grantaipvidcdtbatrheoopiesmnol2MdCadiStYibutcdfM direct cxxnxnercisl edvmtage. the VLDB copyri&t notice md the title of the public&m and its date spar, d notice is given thatcopyingisbypmikonoftkVay~eI)ruB~ Endowment. To copy othawke, or to republish requinr a fee and/or special permission from the BlKlowmau. Eighteen years have passed since Dr. Codd suggested the relational data model in 1970. Many database systems, including commercial ones, use this data model now. In these systems, relational algebra operations , such as scfeci, project and join, are basic operations for processing tuples of databases. Especially , it is well known that join is an expensive operation and many join methods have been proposed so far [Sto'76,Kit83,DeW85]. Among these join methods, hash-partitioned join methods present good performance as shown in [Kit83,DeW84,Sha86]. In [DeW85], it is also shown that the Nest Loop Join Method is not suitable for large databases and the Hybrid Hash Join Method-one of the hash-partitioned join methods-presents the best behavior among the all of the shown algorithms in its figure 3. We may agree with that result when we have to …

/pdf/hash-partitioned-join-method-using-dynamic-destaging-4o1atzyl33.pdf

Hash-Partitioned Join Method Using Dynamic Destaging Strategy

Ferromagnetism and accompanying large negative magnetoresistance in Pb-substituted Bi–Sr–Co–O misfit-layer compound are investigated in detail. Recent structural analysis of (Bi,Pb) 2 Sr 3 Co 2 O 9 , which has been believed to be a Co analogue of Bi 2 Sr 2 CaCu 2 O 8+δ , revealed that it has a more complex structure including a CoO 2 hexagonal layer [T. Yamamoto et al. , Jpn. J. Appl. Phys. 39 (2000) L747]. Pb substitution for Bi not only introduces holes into the conducting CoO 2 layers but also creates a certain amount of localized spins. Ferromagnetic transition appears at T = 3.2 K with small spontaneous magnetization along the c axis, and around the transition temperature large and anisotropic negative magnetoresistance was observed. This compound is the first example which shows ferromagnetic long-range order in a two-dimensional metallic hexagnonal CoO 2 layer.

Ferromagnetism and Large Negative Magnetoresistance in Pb Doped Bi–Sr–Co–O Misfit-Layer Compound

The second version of FDS-R (functional disk system with relational database engine), FDS-RII, which is designed to handle large relations efficiently, is discussed. On FDS-RII, the processing algorithm is selected at run time from two algorithms (nested loop algorithms, grace hash algorithm) by comparing their estimated I/O costs. The processing strategy is discussed in detail. The I/O cost formula is examined by measuring the execution time of a join query on the FDS-RII. With the expanded version of Wisconsin Benchmark, the performance of FDS-RII is measured. FDS-RII attained a high performance level for large relations as compared to other large database systems such as Gamma and Teradata. While FDS uses just one disk and three MC68020s, Teradata uses 40 disks and 20 AMPs and Gamma requires eight disks and 17 VAX 11/750s. >

Query execution for large relations on functional disk system

Join algorithms on KD-tree indexed relations are proposed. The join algorithms are based on a concept called wave. The wave is a set of pages that is the object of joining and that propagates over the relation space in the direction of the join attribute axis. Four basic join algorithms that determine the wave from one of the relations and one algorithm that determines the wave from both relations are proposed. The algorithms are described and extensively analyzed with analytical formulas and simulation results. Then a garbage collection mechanism is introduced that discards the unnecessary data loaded in the main memory and extends the previous basic algorithms with an efficient memory management. It is shown that the proposed algorithms perform the join of very large relations with one scan. >

Mikio Takagi

Papers

Structural Phase Transition and Metallic Behavior in Misfit Layered (Bi, Pb)-Sr-Co-O System

Hash-Partitioned Join Method Using Dynamic Destaging Strategy

Ferromagnetism and Large Negative Magnetoresistance in Pb Doped Bi–Sr–Co–O Misfit-Layer Compound

Query execution for large relations on functional disk system

Join strategies on KD-tree indexed relations