Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases

The problem of similarity search in large time series databases has attracted much attention recently. It is a non-trivial problem because of the inherent high dimensionality of the data. The most promising solutions involve first performing dimensionality reduction on the data, and then indexing the reduced data with a spatial access method. Three major dimensionality reduction techniques have been proposed: Singular Value Decomposition (SVD), the Discrete Fourier transform (DFT), and more recently the Discrete Wavelet Transform (DWT). In this work we introduce a new dimensionality reduction technique which we call Piecewise Aggregate Approximation (PAA). We theoretically and empirically compare it to the other techniques and demonstrate its superiority. In addition to being competitive with or faster than the other methods, our approach has numerous other advantages. It is simple to understand and to implement, it allows more flexible distance measures, including weighted Euclidean queries, and the index can be built in linear time. Received 16 May 2000 / Revised 18 December 2000 / Accepted in revised form 2 January 2001     

Repeated Price Search

Consumers check few sites in online purchases. Previous research and experiments we perform demonstrate that consumers can not calculate the optimal strategy for price search. They use heuristics whose performance is better than random and less than optimal. To investigate online price search performance we survey student online textbook purchases. Students achieve good performance because they start with a good strategy and online market organization of marketplace and meta-search sites. An important factor is that algorithms at sites searched perform calculations that reduce the computational complexity of the search.     

Converting heterogeneous statistical tables on the web to searchable databases

Much of the world’s quantitative data reside in scattered web tables. For a meaningful role in Big Data analytics, the facts reported in these tables must be brought into a uniform framework. Based on a formalization of header-indexed tables, we proffer an algorithmic solution to end-to-end table processing for a large class of human-readable tables. The proposed algorithms transform header-indexed tables to a category table format that maps easily to a variety of industry-standard data stores for query processing. The algorithms segment table regions based on the unique indexing of the data region by header paths, classify table cells, and factor header category structures of two-dimensional as well as the less common multidimensional tables. Experimental evaluations substantiate the algorithmic approach to processing heterogeneous tables. As demonstrable results, the algorithms generate queryable relational database tables and semantic-web triple stores. Application of our algorithms to 400 web tables randomly selected from diverse sources shows that the algorithmic solution automates end-to-end table processing.     

Optimal monolithic configuration for heat integrated ethanol steam reformer

A design concept for optimal design of monolith catalyst is presented through modeling of transport–kinetic interactions in a monolith catalyst. We argue that reactors employing monolithic catalysts should be based on its optimal choice of geometry. In line with that argument, we present a thorough analysis of the geometrical parameters influencing the performance of non-isothermal reactor operation. In this study, an optimal monolith configuration is estimated to be a combination (d_h, t_w) of (0.9 mm, 0.2 mm) for a compact ethanol reformer to produce hydrogen for portable applications where maximum volumetric reactor activity exists. A three-dimensional modeling framework is developed for the resulting optimal monolithic catalyst design that couples the reforming section with a suitable heat source in a recuperative way. As a result, greater ethanol conversion is obtained from the monolith channels near the periphery of the block. The coupling with combustion could predict the formation of cold and hot spots inside the reactor, their nature being dependent on the flow configuration. Further, the effect of altering the feed inlet operating conditions over the variation of ethanol conversion and temperature inside the reactor is also analyzed. The increase in reforming inlet velocity decreases the outlet conversion and shifts the cold spot, forward and deeper in co-flow configuration. The decreasing inlet feed temperature enhances the transfer of heat, eliminating the cold spot.     

New concepts in early failure modelling

The problem of early failure modelling has been discussed comprehensively. Present models have been discussed and new models are suggested. A drawback of failure rate not being finite at t = 0 for some of the present models is eliminated in the suggested models.     

Stepwise Formation of Some Bivalent Metal Chelates with Sodium Alizarin Sulphonate

The stepwise protonation constants of the ligand and the stability constants of Alizarin Red S with some bivalent metal ions, Pb²⁺, Cu²⁺, Zn²⁺, and Hg²⁺ have been determined at 30° and at various ionic strengths viz. 0.02, 0.05, 0.15 and 0.20, maintained by sodium perchlorate solution. The formation of the chelate is evident from the shift between the (i) ligand and (ii) ligand and metal titration curves. From the values of the stepwise protonation constants and metal ligand stability constants at various ionic strengths, thermodynamic formation constants were evaluated by extrapolation to zero ionic strength. The order of stability constants was found to be: Cu > Pb > Zn > Hg