Search

In many applications, users specify target values for certain attributes/features without requiring exact matches to these values in return. Instead, the result is typically a ranked list of 'top k' objects that best match the specified feature values. User subjectivity is an important aspect of such queries, i.e., which objects are relevant to the user and which are not depends on the perception of the user. Due to the subjective nature of top-k queries, the answers returned by the system to an user query often do not satisfy the users need right away, either because the weights and the distance functions associated with the features do not accurately capture the users perception or because the specified target values do not fully capture her information need or both. In such cases, the user would like to refine the query and resubmit it in order to get back a better set of answers. While there has been a lot of research on query refinement models, there is no work that we are aware of on supporting refinement of top-k queries efficiently in a database system. Done naively, each 'refined' query can be treated as a 'starting' query and evaluated from scratch. This paper explores alternative approaches that significantly improve the cost of evaluating refined queries by exploiting the observation that the refined queries are not modified drastically from one iteration to another. Our experiments over a real-life multimedia data set show that the proposed techniques save more than 80 percent of the execution cost of refined queries over the naive approach and is more than an order of magnitude faster than a simple sequential scan. Index Terms--Multidimensional indexing, k-nearest neighbor search, similarity queries, query refinement, relevance feedback.

Similarity search in large time series databases has attracted much research interest recently. It is a difficult problem because of the typically high dimensionality of the data. The most promising solutions involve performing dimensionality reduction on the data, then indexing the reduced data with a multidimensional index structure. Many dimensionality reduction techniques have been proposed, including Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and the Discrete Wavelet Transform (DWT). In this article, we introduce a new dimensionality reduction technique, which we call Adaptive Piecewise Constant Approximation (APCA). While previous techniques (e.g., SVD, DFT and DWT) choose a common representation for all the items in the database that minimizes the global reconstruction error, APCA approximates each time series by a set of constant value segments of varying lengths such that their individual reconstruction errors are minimal. We show how APCA can be indexed using a multidimensional index structure. We propose two distance measures in the indexed space that exploit the high fidelity of APCA for fast searching: a lower bounding Euclidean distance approximation, and a non-lower-bounding, but very tight, Euclidean distance approximation, and show how they can support fast exact searching and even faster approximate searching on the same index structure. We theoretically and empirically compare APCA to all the other techniques and demonstrate its superiority. Categories and Subject Descriptors: H.2.4 [Database Management]: Systems--multimedia databases; H.3.1 [Information Storage and Retrieval]: Content Analysis and Indexing--indexing methods; H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval--search process General Terms: Algorithms, Design, Performance Additional Key Words and Phrases: Dimensionality reduction, indexing, time-series similarity retrieval

Solid solution of nickel ferrite (NiFe2O4) and barium titanate (BaTiO3), (100-x)BaTiO3-(x) NiFe2O4 has been prepared by solid state reaction. Compressive strain is developed in NiFe2O4 due to mutual structural interaction across the interface of NiFe2O4 and BaTiO3 phases. Quantitative analysis of X-ray diffraction and X-ray photo electron spectrum suggest mixed spinel structure of NiFe2O4. A systematic study of composition dependence of composite indicates BaTiO3 causes a random distribution of Fe and Ni cations among octahedral and tetrahedral sites during non-equilibrium growth of NiFe2O4. The degree of inversion decreases monotonically from 0.97 to 0.75 with increase of BaTiO3 content. Temperature dependence of magnetization has been analyzed by four sublattice model to describe complex magnetic exchange interactions in mixed spinel phase. Curie temperature and saturation magnetization decrease with increase of BaTiO3 concentration. Enhancement of strain and larger occupancy of Ni2+ at tetrahedral site increase coercivity up to 200 Oe. Magnetostructual coupling induced by BaTiO3 improves coercivity in NiFe2O4. An increase in the demagnetization and homogeneity in magnetization process in NiFe2O4 is observed due to the interaction with diamagnetic BaTiO3. [ABSTRACT FROM AUTHOR]

Ferromagnetic BiFeO3 nanocrystals of average size 11 nm were used to form nanocomposites (x)BiFeO3/(100 - x)NiO, x = 0, 20, 40, 50, 60, 80, and 100 by simple solvothermal process. The ferromagnetic BiFeO3 nanocrystals embedded in antiferromagnetic NiO nanostructures were confirmed from X-ray diffraction and transmission electron microscope studies. The modification of cycloidal spin structure of bulk BiFeO3 owing to reduction in particle size compared to its spin spiral wavelength (62 nm) results in ferromagnetic ordering in pure BiFeO3 nanocrystals. High Neel temperature (TN) of NiO leads to significant exchange bias effect across the BiFeO3/NiO interface at room temperature. A maximum exchange bias field of 123.5 Oe at 300K for x = 50 after field cooling at 7 kOe has been observed. The exchange bias coupling causes an enhancement of coercivity up to 235 Oe at 300 K. The observed exchange bias effect originates from the exchange coupling between the surface uncompensated spins of BiFeO3 nanocrystals and NiO nanostructures. [ABSTRACT FROM AUTHOR]

The BiFeO3 nanoparticles, having an average size of 13 nm, were synthesized by a simple sol-gel method. The samples possess single phase up to 2% Eu doping at the Bi site. The uncompensated spin moments on the surface and the modification of cycloidal spin structure due to the small size (13 nm) result in a ferromagnetic phase of the BiFeO3 nanoparticles. The successful doping of magnetically active Eu3+ ions in BiFeO3 nanoparticles improves the ferromagnetic property. The similar dependency of saturation magnetization, coercive field, and dielectric constant on Eu doping concentration reveals that a correlation between magnetic and dielectric properties exists in Eu-doped BiFeO3 nanoparticles. [ABSTRACT FROM AUTHOR]