Time Complexity

ÐEfficient and effective buffering of disk blocks in main memory is critical for better file system performance due to a wide speed gap between main memory and hard disks. In such a buffering system, one of the most important design decisions is the block replacement policy that determines which disk block to replace when the buffer is full. In this paper, we show that there exists a spectrum of block replacement policies that subsumes the two seemingly unrelated and independent Least Recently Used (LRU) and Least Frequently Used (LFU) policies. The spectrum is called the LRFU (Least Recently/Frequently Used) policy and is formed by how much more weight we give to the recent history than to the older history. We also show that there is a spectrum of implementations of the LRFU that again subsumes the LRU and LFU implementations. This spectrum is again dictated by how much weight is given to recent and older histories and the time complexity of the implementations lies between O(1) (the time complexity of LRU) and ylog P n (the time complexity of LFU), where n is the number of blocks in the buffer. Experimental results from trace-driven simulations show that the performance of the LRFU is at least competitive with that of previously known policies for the workloads we considered.

In this paper, we introduce a new threshold signature RSA-typed scheme. The proposed scheme has the characteristics of un-forgeable and robustness in random oracle model. Also, signature generation and verification is entirely non-interactive. In addition, the length of the entity signature participate is restricted by a steady times of the length of the RSA signature modulus. Also, the signing process of the proposed scheme is more efficient in terms of time complexity and interaction.

Abstract— Time Slot Interchange and Signal Separator (TSISS) network was introduced to reduce the time slot requirement of TST architecture. In the switching algorithm of the TSISS network, an ad-jacency matrix was formed and Breath-first search (BFS) or Depth-first ...

We analyse the key algorithms of data and information fusion from a linguistic point of view, and show that they fall into two paradigms: the primarily syntactic, and the primarily semantic. We propose an alternative grammatical paradigm which exploits the ability of grammar to combine syntactic inference with semantic representation. We generalize the concept of formal generative grammar to include multiple rule classes each having a topology and a base vocabulary. A generalized Chomsky hierarchy is defined. Analysing fusion algorithms in terms of grammatical representations, we find that most (including multiple hypothesis tracking) can be expressed in terms of conventional regular grammars. Situation analysis, however, is commonly attempted using first order predicate logic, which while expressive, is recursively enumerable and so scales badly. We argue that the core issue in situation assessment is force deployment assessment, the extraction and scoring of hypotheses of the force deployment history, each of which is a multiresolution account of the activities, groupings and interactions of force components. The force deployment history represents these relationships at multiple levels of granularity and is expressed over time and space. We provide a grammatical approach for inferring such histories, and show that they can be estimated accurately and scalably. We employ a generalized context-free grammar incorporating both sequence and multiset productions. Elaborating [D. McMichael, G. Jarrad, S. Williams, M.

This work addresses the problem of low power design in high-level synthesis in the scenario of the resources operating at multiple voltages. The problem of resource-and-latency constrained scheduling is tackled and a novel methodology for scheduling as well as design space exploration is proposed. The proposed methodology achieves maximal power reduction of functional units by identifying the maximal parallelism of power hungry operators in resource-constrained/time-constrained designs. A novel scheme is devised for recognizing the ''zones'' of parallel operators that result in maximal power savings when rescheduled to lower voltages. The proposed methodology is developed in the framework of a modified stochastic evolution mechanism in order to tame the computational complexity. The proposed scheduling technique is extremely fast and it runs in Oðn 2 Þ; where n is the number of the nodes in the data flow graph of the design. This is the fastest reported time of scheduling algorithms for resourceand-latency constrained scheduling with resources operating at multiple voltages. The algorithm produces results within accuracy of 3-5% of the integer-linear-programming based (i.e., exponential-timecomplexity) method. The details and the analysis of the results on the standard high-level synthesis benchmarks are provided. Further, analysis of schedules is given for considering generation of control signals for switching the resources. This is the first work to consider such analysis besides proposing an efficient algorithm. r

Let S be a set of elements. We say that a collection C of subsets of S has the consecutive ones property if there exist a linear order on S and a 0-1 matrix M , where M ij = 1 if and only if the jth element is in the ith set in C, such that all 1's appear consecutively in each row of M . We say that a set X in a collection C is hit by a subset S ′ ⊆ S if X ∩ S ′ = ∅. Let C r (red collection) and C b (blue collection) be two collections of subsets of S respectively. The red-blue hitting set problem asks for a subset S ′ ⊆ S such that all sets in the blue collection must be hit by S ′ , while the number of sets in the red collection hit by S ′ has to be minimum. We present a shortest-path based algorithm with time complexity O(|C b ||S| + |C r ||S| + |S| 2 ) for this problem with C r ∪ C b having the consecutive ones property, which improves the previous time bound O(|C r ||C b ||S| 2 ) by Dom et al. .

Periodicity mining is used for predicting trends in time series data. Discovering the rate at which the time series is periodic has always been an obstacle for fully automated periodicity mining. Existing periodicity mining algorithms assume that the periodicity rate (or simply the period) is user-specified. This assumption is a considerable limitation, especially in time series data where the period is not known a priori. In this paper, we address the problem of detecting the periodicity rate of a time series database. Two types of periodicities are defined, and a scalable, computationally efficient algorithm is proposed for each type. The algorithms perform in Oðn log nÞ time for a time series of length n. Moreover, the proposed algorithms are extended in order to discover the periodic patterns of unknown periods at the same time without affecting the time complexity. Experimental results show that the proposed algorithms are highly accurate with respect to the discovered periodicity rates and periodic patterns. Real-data experiments demonstrate the practicality of the discovered periodic patterns.

Recently there has been a great deal of interest in performance evalution of parallel simulation. Most work is devoted to the time complexity and assumes that the amount of memory available for parallel simulation is unlimited. This paper studies the space complexity of parallel simulation. Our goal is to design an efficient memory management protocol which guarantees that the memory consumption of parallel simulation is of the same order as sequential simulation. (Such an algorithm is referred to as a optimal .) First, we derive the relationships among the space complexities of sequential simulation, Chandy-Misra simulation [2], and Time Warp simulation [7]. We show that Chandy-Misra may consume more storage than sequential simulation, or vice versa. Then we show that Time Warp never consumes less memory than sequential simulation. Then we describe cancelback , an optimal Time Warp memory management protocol proposed by Jefferson. Although cancelback is considered to be complete so...

In Image Compression, the researchers' aim is to reduce the number of bits required to represent an image by removing the spatial and spectral redundancies. Recently discrete wavelet transform and wavelet packet has emerged as popular techniques for image compression. The wavelet transform is one of the major processing components of image compression. The result of the compression changes as per the basis and tap of the wavelet used. It is proposed that proper selection of mother wavelet on the basis of nature of images, improve the quality as well as compression ratio remarkably. We suggest the novel technique, which is based on wavelet packet best tree based on Threshold Entropy with enhanced run-length encoding. This method reduces the time complexity of wavelet packets decomposition as complete tree is not decomposed. Our algorithm selects the sub-bands, which include significant information based on threshold entropy. The enhanced run length encoding technique is suggested provides better results than RLE. The result when compared with JPEG-2000 proves to be better.

Inherent to the wireless sensor networks are the two major problems of the broadcasting vulnerability, the limited computational capability and power budget. Even though security is a must in most applications, current sophisticated security protocols are not amenable to the primitiveness of the sensors. In this paper, we introduce a novel security protocol for wireless network of sensors that is very secure, yet simple and efficient. At the core of our security protocol is a simple and fast stream cipher cryptosystem that utilizes permutation vectors as encryption keys, forcing an intruder to a brute-force time complexity of Ω(2 n). In addition, our mechanism alleviates the effect of sensor capture, via its synchronized re-keying feature. In addition to the encryption efficiency, our system utilizes the group deployment of newly joining sensors for sensors power budgeting considerations. Experimental results show very promising future of our system in the wireless networks domain, excelling over other peers of modern cryptosystems (AES, DES, TripleDES), especially in the power budget arena.

In this work, the performance of probabilistic algebraic attacks is compared to classical (fast) algebraic attacks in the context of their application to certain linear beedback shift register (LFSR)-based stream ciphers. Using some results from coding theory it is shown that in terms of time complexity classical deterministic algebraic attacks are in general a more efficient cryptanalytic tool, unless the filtering function F : GF (2) n ! GF (2) m has such a nonrandom structure that its cryptographic use is presumably refutable anyway.

We present an efficient Hough transform for automatic detection of cylinders in point clouds. As cylinders are one of the most frequently used primitives for industrial design, automatic and robust methods for their detection and fitting are essential for reverse engineering from point clouds. The current methods employ automatic segmentation followed by geometric fitting, which requires a lot of manual interaction during modelling. Although Hough transform can be used for automatic detection of cylinders, the required 5D Hough space has a prohibitively high time and space complexity for most practical applications. We address this problem in this paper and present a sequential Hough transform for automatic detection of cylinders in point clouds. Our algorithm consists of two sequential steps of low dimensional Hough trans- forms. The first step, called Orientation Estimation, uses the Gaussian sphere of the input data and performs a 2D Hough Transform for finding strong hypotheses ...

We present a new machine learning method that, given a set of training examples, induces a definition of the target concept in terms of a hierarchy of intermediate concepts and their definitions. This effectively decomposes the problem into smaller, less complex problems. The method is inspired by the Boolean function decomposition approach to the design of switching circuits. To cope with high time complexity of finding an optimal decomposition, we propose a suboptimal heuristic algorithm. The method, implemented in program HINT (Hierarchy INduction Tool), is experimentally evaluated using a set of artificial and real-world learning problems. In particular, the evaluation addresses the generalization property of decomposition and its capability to discover meaningful hierarchies. The experiments show that HINT performs well in both respects.

Betweenness centrality measure is often used in social and computer communication networks to estimate the potential monitoring and control capabilities a vertex may have on data flowing in the network. In this paper we define the Routing Betweenness Centrality (RBC) measure which generalizes previously well known Betweenness measures such as the Shortest Path Betweenness, Flow Betweenness, and Traffic Load Centrality by considering network flows created by arbitrary loop-free routing strategies.

The time complexity for testing whether an n-by-n real matrix is a P-matrix is reduced from O(2n n 3) to O(2 n ) by applying recursively a criterion for P-matrices based on Schur complementation. A Matlab program implementing the associated algorithm is provided.

We study the problem of sequencing n jobs on a three-stage¯owshop with multiple identical machines at each stage (a¯exiblē owshop). The objective is to minimize the makespan. Since the problem is strongly NP-complete, we develop and compare several heuristic procedures of time complexity O n log n : We were successful in deriving the worst case performance bound of one procedure. We have also developed several lower bounds that serve as datum for comparison; the lower bound used in the evaluation is always the best among them. Extensive experiments were conducted to evaluate the performance of the proposed procedures, and preferences are concluded based on their average performance.

In field of Computer Science and Mathematics, sorting algorithm is an algorithm that puts elements of a list in a certain order i.e. ascending or descending. Sorting is perhaps the most widely studied problem in computer science and is frequently used as a benchmark of a system's performance. This paper presented the comparative performance study of four sorting algorithms on different platform. For each machine, it is found that the algorithm depends upon the number of elements to be sorted. In addition, as expected, results show that the relative performance of the algorithms differed on the various machines. So, algorithm performance is dependent on data size and there exists impact of hardware also.

Efficient ray shooting algorithm is inherently required by many computer graphics algorithms, particularly in image synthesis. Practical ray shooting algorithms aiming at the average-case complexity use some underlying spatial data structure such as -tree. We show the new termi- nation criteria algorithm that improves the space and time complexity of the -tree construction. It provides efficient ray-shooting queries and does

Using doubly linked list in embedded systems can be expensive. Although doubly linked is efficient in terms of time complexity and highly flexible in itself, enabling easy traversal and update, but it often demands more memory space compared to other list data strucurethis is as a result of the space required for the extra pointers, doubling the amount needed for a singly linked list. In this paper, we introduce the S-linked lista hybrid of the concept of the singly linked list and the circular linked list. The hybrid gives a data structure that is similar to the unrolled linked list, but rather than have an array in each node, we have a singly linked list. An analysis of the space complexity and assymptotic time complexity of the algorithm was carried out.

Functionality of electronic commerce enabling technologies is dependent upon pervasive networks that a large number of applications share. Given the scarcity of network resources, it is imperative to allocate existing resources based on the actual needs of its users. In this paper, we study a two sided matching algorithm that enables such an allocation using tools from matching literature. Network system deploys intelligent agents that negotiate terms of allocation assignments with other intelligent agents that represent the users. We report the time complexity of the algorithm and give an example on how the mechanism performs.

Abstract—There are many classical algorithms for finding routing in FPGA. But Using DNA computing we can solve the routes efficiently and fast. The run time complexity of DNA algorithms is much less than other classical algorithms which are used for solving routing in FPGA. The ...

... 7 Conclusions In this paper, we have proposed a geometry-based image representation scheme and an algo-rithm/function for retrieving i&D images of relevance (based on spatial similarity) to ... The algorithm is robust-in the sense that it can recognize translation and scale ...

Bubble-sort graphs are variants of Cayley graphs. A bubble-sort graph is suitable as a topology for massively parallel systems because of its simple and regular structure. Therefore, in this study, we focus on n-bubble-sort graphs and propose an algorithm to obtain n-1 disjoint paths between two arbitrary nodes in time bounded by a polynomial in n, the degree of the graph plus one. We estimate the time complexity of the algorithm and the sum of the path lengths after proving the correctness of the algorithm. In addition, we report the results of computer experiments evaluating the average performance of the algorithm.

Workstations and personal computers are increasingly being used for applications with real-time characteristics such as speech understanding and synthesis, media computations and I/O, and animation, often concurrently executed with traditional non-real-time workloads. This paper presents a system that can schedule multiple independent activities so that: . activities can obtain minimum guaranteed execution rates with application-specified reservation granularities via CPU Reservations, . CPU Reservations, which are of the form "reserve X units of time out of every Y units", provide not just an average case execution rate of X/Y over long periods of time, but the stronger guarantee that from any instant of time, by Y time units later, the activity will have executed for at least X time units, . applications can use Time Constraints to schedule tasks by deadlines, with on-time completion guaranteed for tasks with accepted constraints, and . both CPU Reservations and Time Con...

There is growing interest in scaling up the widely-used decision-tree learning algorithms to very large data sets. Although numerous diverse techniques have been proposed, a fast tree-growing algorithm without substantial decrease in accuracy and substantial increase in space complexity is essential. In this paper, we present a novel, fast decision-tree learning algorithm that is based on a conditional independence assumption. The new algorithm has a time complexity of O(m · n), where m is the size of the training data and n is the number of attributes. This is a significant asymptotic improvement over the time complexity O(m · n 2 ) of the standard decision-tree learning algorithm C4.5, with an additional space increase of only O(n). Experiments show that our algorithm performs competitively with C4.5 in accuracy on a large number of UCI benchmark data sets, and performs even better and significantly faster than C4.5 on a large number of text classification data sets. The time complexity of our algorithm is as low as naive Bayes'. Indeed, it is as fast as naive Bayes but outperforms naive Bayes in accuracy according to our experiments. Our algorithm is a core tree-growing algorithm that can be combined with other scaling-up techniques to achieve further speedup.

Finding the shortest paths in a schedule-based public transit network is similar to finding the shortest paths in a network with multi-costs constrained and time-window constraints. We consider a schedulebased transit network, where each node in the network has a list of scheduled departure times for transit vehicles. The objective of this paper is to find out the first K shortest paths in a schedule-based transit network. An algorithm with a time complexity of OðS 2 L 3 ÞþOðL 4 SR max l ÞþOðSLR max l K ðL 3 logðL 3 ÞþL 3 ÞÞ is proposed to enumerate these paths. We compare the time complexity of our algorithm with that of two existing path finding algorithms. We show that our algorithm has a better performance in computational time complexity than the two existing path finding algorithms, though it is only applicable if the number of transfers does not exceed 2. Also, for large sized real transit network, our algorithm determines the K shortest paths in a much shorter time frame than the existing two algorithms.

We are given an integer n and a real-valued function w(i, j) defined for integers 0 5 i <j 5 n and with the property that w(i,-,, j,,) + w(il, jl) I w(iO, jl) + w( il, J,-J for 0 5 i, < il < j,, < j, I n. The concave least-weight subsequence problem istofindanintegerkrlandasequenceofintegersO=I,<I,~ ... <I,-,< 1, = n such that Zf:iw(/,, li+l) is minim&d. One application of this problem is determining optimal line breaks in a text formatting system. D. S. Hirschberg and L. L. Larmore (SIAM J. Comput. 16 (1987), 628-638) showed that the concave least-weight subsequence problem can be solved in 0( n log n) time and that if a certain extra condition is imposed it can be solved iu O(n) time. Here we show that the concave least weight subsequence problem can always be solved in 0(n) time, without any extra conditions. Q 1988 .ademi~ press, Inc.

Quite a few recent models are rapidly introducing new concepts describing different levels of consciousness. This situation is getting confusing because some theorists formulate their models without making reference to existing views, redundantly adding complexity to an already difficult problem. In this paper I present and compare nine such models to highlight points of convergence and divergence. Two aspects of consciousness seem especially important: perception of self in time and complexity of self-representations. To this I add frequency of self-focus, amount of self-related information, and accuracy of self-knowledge. Overall, I conclude that many novel concepts (e.g., reflective, primary, core, extended, recursive, and minimal consciousness) are useful in helping us distinguish between delicate variations in consciousness and in clarifying theoretical issues that have been intensely debated in the scientific literature-e.g., consciousness in relation to mirror selfrecognition and language.

The non-blocking work-stealing algorithm of Arora, Blumofe, and Plaxton (henceforth ABP work-stealing) is on its way to becoming the multiprocessor load balancing technology of choice in both industry and academia. This highly efficient scheme is based on a collection of array-based doubleended queues (deques) with low cost synchronization among local and stealing processes. Unfortunately, the algorithm's synchronization protocol is strongly based on the use of fixed size arrays, which are prone to overflows, especially in the multiprogrammed environments for which they are designed. We present a work-stealing deque that does not have the overflow problem. The only ABP-style work-stealing algorithm that eliminates the overflow problem is the list-based one presented by Hendler, Lev and Shavit. Their algorithm indeed deals with the overflow problem, but it is complicated, and introduces a trade-off between the space and time complexity, due to the extra work required to maintain the list. Our new algorithm presents a simple lock-free work-stealing deque, which stores the elements in a cyclic array that can grow when it overflows. The algorithm has no limit other than integer overflow (and the system's memory size) on the number of elements that may be on the deque, and the total memory required is linear in the number of elements in the deque.

Comparing gene orders in completely sequenced genomes is a standard approach to locate clusters of functionally associated genes. Often, gene orders are modeled as permutations. Given k permutations of n elements, a k-tuple of intervals of these permutations consisting of the same set of elements is called a common interval. We consider several problems related to common intervals in multiple genomes. We present an algorithm that finds all common intervals in a family of genomes, each of which might consist of several chromosomes. We present another algorithm that finds all common intervals in a family of circular permutations. A third algorithm finds all common intervals in signed permutations. We also investigate how to combine these approaches. All algorithms have optimal worst-case time complexity and use linear space.

We construct an exact algorithm for the Hamiltonian cycle problem in planar graphs with worst case time complexity O(c √ n ), where c is some fixed constant that does not depend on the instance. Furthermore, we show that under the exponential time hypothesis, the time complexity cannot be improved to O(c o( √ n) ).

The effectiveness of an algorithm is measured from the amount of time and space required by the algorithm. The objective of this study was to measure the time complexity of the ANFIS algorithm. The time complexity is calcultaed by counting the number of loops and operators used in a procedure. In addition, calculate the time required to running the ANFIS algorithm with given a number of input. The size of the input (n) is very influential as it states the amount of data processed. The results of the profiling showed that ANFIS has the asymptotic time complexity O(n).

Finding clusters in data, especially high dimensional data, is challenging when the clusters are of widely differing shapes, sizes, and densities, and when the data contains noise and outliers. We present a novel clustering technique that addresses these issues. Our algorithm first finds the nearest neighbors of each data point and then redefines the similarity between pairs of points in terms of how many nearest neighbors the two points share. Using this definition of similarity, our algorithm identifies core points and then builds clusters around the core points. The use of a shared nearest neighbor definition of similarity alleviates problems with varying densities and high dimensionality, while the use of core points handles problems with shape and size. While our algorithm can find the "dense" clusters that other clustering algorithms find, it also finds clusters that these approaches overlook, i.e., clusters of low or medium density which represent relatively uniform regions "surrounded" by non-uniform or higher density areas. We experimentally show that our algorithm performs better than traditional methods (e.g., K-means, DBSCAN, CURE) on a variety of data sets: KDD Cup '99 network intrusion data, NASA Earth science time series data, and two dimensional point sets. The run-time complexity of our technique is O(n 2 ) if the similarity matrix has to be constructed. However, we discuss a number of optimizations that allow the algorithm to handle large data sets efficiently.

The increasing sophistication of computer technology with its capacity to collect, analyze and disseminate information on individuals posses a great challenge to society today. The Collection of information about users has become a lucrative business. The extensive and rapid adoption of computers by business organizations and society in general has aggravated existing ethical issues and led to the emergence of new ethical issues. The purpose of this paper is to interrogate the ethical issues in a computerized society from 1980s to the 2000s. The paper looked at discussions and issues raised from 1980 to 2000.When society entered the twenty-first century, many looked to technology and it's potential to solve human problems. The hope that technology can solve the problems of this century seems justified when one considers the powerful nature of technology in transforming life. As society develops, we must be both hopeful as well as cautious about the technologies we adopt and the ways in which we use them as they affect us in both good and bad ways. Human values are transformed in both ways, good and bad. The research revealed that computer technology poses a great challenge to human ethics and society at large. Computer technology continue to change and reform society. The ethics of today which society holds dear to is not the same ethics tomorrow as a result of computer technological changes. Ethical principles should be developed and awareness created to prevent unethical computer and internet practices.

Software testing is a dual purpose process that reveals defects and is used to evaluate quality attributes of the software, such as, reliability, security, usability, and correctness. The critical problem in software testing is time complexity. In this paper we have developed a strategy to reduce the time complexity of database testing by using Fast Searching Algorithm known as "Grover' s Algorithm" for unsorted database The significance of said algorithm is to minimize time and cost both by using a formal algorithm. Another main benefit of such algorithm is that the complexity is too less than other searching algorithms in use.

The Hough transform provides a robust technique for skew detection in document images, but suffers from high time complexity which becomes prohibitive for detecting skew in large documents. Analysis of time complexity on various stages of skew detection process is carried out in this paper. A complete skew detection and correction process is divided into three parts: a preprocessing stage using a simplified form of block adjacency graph (BAG), voting process using the Hough transform and de-skewing the image using rotation. Skew correction phase, which is hitherto a neglected area, is analysed for the quality of de-skewed images with respect to the type of rotation. Fast algorithms for all the three stages are presented and exhaustive analysis on time complexity is conducted. It is shown that the overall time taken for the whole process is less than one second even for very large documents. It is also observed that time taken in rotation is as significant as in skew detection which is reduced with the help of fast algorithms using integer operations. While the BAG algorithm is found to be effective for documents with Roman script, it does not provide satisfactory results for Indian scripts where headline is a part of a script.

Sorting is the basic operation in most of the applications of computer science. Sorting means to arrange data in particular order inside computer. In this paper we have discussed performance of different sorting algorithms with their advantages and disadvantages. This paper also represents the application areas for different sorting algorithms. Main goal of this paper is to compare the performance of different sorting algorithms based on different parameters. Keywords— Algorithm, Time Complexity, Space Complexity

Sugiyama's algorithm for layered graph drawing is very popular and commonly used in practical software. The extensive use of dummy vertices to break long edges between non-adjacent layers often leads to unsatisfying performance. The worst-case running-time of Sugiyama's approach is O(|V ||E| log |E|) requiring O(|V ||E|) memory, which makes it unusable for the visualization of large graphs. By a conceptually simple new technique we are able to keep the number of dummy vertices and edges linear in the size of the graph without increasing the number of crossings. We reduce the worst-case time complexity of Sugiyama's approach by an order of magnitude to O((|V | + |E|) log |E|) requiring O(|V | + |E|) space.

This paper presents a novel evolutionary optimization strategy based on the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). This new approach is intended to reduce the number of generations required for convergence to the optimum. Reducing the number of generations, i.e., the time complexity of the algorithm, is important if a large population size is desired: (1) to reduce the effect of noise;

Sorting is the basic operation in most of the applications of computer science. Sorting means to arrange data in particular order inside computer. In this paper we have discussed performance of different sorting algorithms with their advantages and disadvantages. This paper also represents the application areas for different sorting algorithms. Main goal of this paper is to compare the performance of different sorting algorithms based on different parameters.

Previous survey research revealed that consumers perceived online shopping and buying to be a time saving practice. Para- doxically, they only rarely reported using that tactic to save time, introducing a contradiction. Focus group research and unstruc- tured interviews among active, time-pressured consumers pro- vide partial solutions to the puzzle. Two types of online shopping benefits appeal to two types of consumers: Those experiencing situational time pressure respond well to &quot;time-saving&quot; appeals. Another, larger proportion, whose personal inclinations or per- sonality traits result in time pressure are more interested in do- ing more tasks quickly or engaging in polychronic activities. These findings suggest online merchants simultaneously promote both time-saving and quicker accomplishment of more tasks. Though they are two sides of the same coin, time-pressured con- sumers tend to see only one side or the other. Both types of time-pressured shoppers eschew Website...