Boosting the Basic Counting on Distributed Streams

We revisit the classic basic counting problem in the distributed streaming model that was studied by Gibbons and Tirthapura (GT). In the solution for maintaining an $(\epsilon,\delta)$-estimate, as what GT's method does, we make the following new contributions: (1) For a bit stream of size $n$, where each bit has a probability at least $\gamma$ to be 1, we exponentially reduced the average total processing time from GT's $\Theta(n \log(1/\delta))$ to $O((1/(\gamma\epsilon^2))(\log^2 n) \log(1/\delta))$, thus providing the first sublinear-time streaming algorithm for this problem. (2) In addition to an overall much faster processing speed, our method provides a new tradeoff that a lower accuracy demand (a larger value for $\epsilon$) promises a faster processing speed, whereas GT's processing speed is $\Theta(n \log(1/\delta))$ in any case and for any $\epsilon$. (3) The worst-case total time cost of our method matches GT's $\Theta(n\log(1/\delta))$, which is necessary but rarely occurs in our method. (4) The space usage overhead in our method is a lower order term compared with GT's space usage and occurs only $O(\log n)$ times during the stream processing and is too negligible to be detected by the operating system in practice. We further validate these solid theoretical results with experiments on both real-world and synthetic data, showing that our method is faster than GT's by a factor of several to several thousands depending on the stream size and accuracy demands, without any detectable space usage overhead. Our method is based on a faster sampling technique that we design for boosting GT's method and we believe this technique can be of other interest.Comment: 32 page

On Longest Repeat Queries Using GPU

Repeat finding in strings has important applications in subfields such as computational biology. The challenge of finding the longest repeats covering particular string positions was recently proposed and solved by \.{I}leri et al., using a total of the optimal $O(n)$ time and space, where $n$ is the string size. However, their solution can only find the \emph{leftmost} longest repeat for each of the $n$ string position. It is also not known how to parallelize their solution. In this paper, we propose a new solution for longest repeat finding, which although is theoretically suboptimal in time but is conceptually simpler and works faster and uses less memory space in practice than the optimal solution. Further, our solution can find \emph{all} longest repeats of every string position, while still maintaining a faster processing speed and less memory space usage. Moreover, our solution is \emph{parallelizable} in the shared memory architecture (SMA), enabling it to take advantage of the modern multi-processor computing platforms such as the general-purpose graphics processing units (GPU). We have implemented both the sequential and parallel versions of our solution. Experiments with both biological and non-biological data show that our sequential and parallel solutions are faster than the optimal solution by a factor of 2--3.5 and 6--14, respectively, and use less memory space.Comment: 14 page

CloudTree: A Library to Extend Cloud Services for Trees

In this work, we propose a library that enables on a cloud the creation and management of tree data structures from a cloud client. As a proof of concept, we implement a new cloud service CloudTree. With CloudTree, users are able to organize big data into tree data structures of their choice that are physically stored in a cloud. We use caching, prefetching, and aggregation techniques in the design and implementation of CloudTree to enhance performance. We have implemented the services of Binary Search Trees (BST) and Prefix Trees as current members in CloudTree and have benchmarked their performance using the Amazon Cloud. The idea and techniques in the design and implementation of a BST and prefix tree is generic and thus can also be used for other types of trees such as B-tree, and other link-based data structures such as linked lists and graphs. Preliminary experimental results show that CloudTree is useful and efficient for various big data applications

Shortest Unique Substring Query Revisited

We revisit the problem of finding shortest unique substring (SUS) proposed recently by [6]. We propose an optimal $O(n)$ time and space algorithm that can find an SUS for every location of a string of size $n$. Our algorithm significantly improves the $O(n^2)$ time complexity needed by [6]. We also support finding all the SUSes covering every location, whereas the solution in [6] can find only one SUS for every location. Further, our solution is simpler and easier to implement and can also be more space efficient in practice, since we only use the inverse suffix array and longest common prefix array of the string, while the algorithm in [6] uses the suffix tree of the string and other auxiliary data structures. Our theoretical results are validated by an empirical study that shows our algorithm is much faster and more space-saving than the one in [6]

Time-decaying Sketches for Robust Aggregation of Sensor Data

We present a new sketch for summarizing network data. The sketch has the following properties which make it useful in communication-efficient aggregation in distributed streaming scenarios, such as sensor networks: the sketch is duplicate insensitive, i.e., reinsertions of the same data will not affect the sketch and hence the estimates of aggregates. Unlike previous duplicate-insensitive sketches for sensor data aggregation [S. Nath et al., Synposis diffusion for robust aggregation in sensor networks, in Proceedings of the 2nd International Conference on Embedded Network Sensor Systems, (2004), pp. 250–262], [J. Considine et al., Approximate aggregation techniques for sensor databases, in Proceedings of the 20th International Conference on Data Engineering (ICDE), 2004, pp. 449–460], it is also time decaying, so that the weight of a data item in the sketch can decrease with time according to a user-specified decay function. The sketch can give provably approximate guarantees for various aggregates of data, including the sum, median, quantiles, and frequent elements. The size of the sketch and the time taken to update it are both polylogarithmic in the size of the relevant data. Further, multiple sketches computed over distributed data can be combined without loss of accuracy. To our knowledge, this is the first sketch that combines all the above properties

Charge transport in nanoscale vertical organic semiconductor pillar devices

We report charge transport measurements in nanoscale vertical pillar structures incorporating ultrathin layers of the organic semiconductor poly(3-hexylthiophene)(P3HT). P3HT layers with thickness down to 5 nm are gently top-contacted using wedging transfer, yielding highly reproducible, robust nanoscale junctions carrying high current densities (up to $10^6$ A/m$^2$). Current-voltage data modeling demonstrates excellent hole injection. This work opens up the pathway towards nanoscale, ultrashort-channel organic transistors for high-frequency and high-current-density operation.Comment: 30 pages, 8 figures, 1 tabl