Advertisement

Municipal Broadband Wireless Networks

"...people lack many things: jobs, shelter, food, health care and drinkable water. Today, being cut off from basic telecommunications services is a hardship almost as acute as these other deprivations, and may indeed reduce the chances of finding remedies to them."---UN Secretary General, Kofi Annan, in a keynote address to the International Telecommunication Union, Oct. 9, 1999.

MapReduce: Simplified Data Processing on Large Clusters

MapReduce is a programming model and an associated implementation for processing and generating large datasets that is amenable to a broad variety of real-world tasks. Users specify the computation in terms of a map and a reduce function, and the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks. Programmers find the system easy to use: more than ten thousand distinct MapReduce programs have been implemented internally at Google over the past four years, and an average of one hundred thousand MapReduce jobs are executed on Google's clusters every day, processing a total of more than twenty petabytes of data per day.

Near-Optimal Hashing Algorithms for Approximate Nearest Neighbor in High Dimensions

In this article, we give an overview of efficient algorithms for the approximate and exact nearest neighbor problem. The goal is to preprocess a dataset of objects (e.g., images) so that later, given a new query object, one can quickly return the dataset object that is most similar to the query. The problem is of significant interest in a wide variety of areas.

Technical Perspective: Finding a Good Neighbor, Near and Fast

You haven't read it yet, but you can already tell this article is going to be one long jumble of words, numbers, and punctuation marks. Indeed, but look at it differently, as a text classifier would, and you will see a single point in high dimension, with word frequencies acting as coordinates. Or take the background on your flat panel display: a million colorful pixels teaming up to make quite a striking picture. Yes, but also one single point in 106-dimensional space--that is, if you think of each pixel's RGB intensity as a separate coordinate. In fact, you don't need to look hard to find complex, heterogeneous data encoded as clouds of points in high dimension. They routinely surface in applications as diverse as medical imaging, bioinformatics, astrophysics, and finance.

The Centrality and Prestige of CACM

Journal rankings identify the most respected publications in a field, and can influence which sources to read to remain current, as well as which journals to target when publishing. Ranking studies also help track the progress of the field, identifying core journals and research topics, and tracking changes in these topics and perceptions over time. Past journal ranking studies have consistently found Communications of the ACM (CACM) to be very highly respected within the IS discipline. However, the exact nature of its relationships to other IS journals has not been thoroughly examined. In this article, we report a social network analysis (SNA) of 120 journals for the purpose of exploring in detail CACM's position within the IS journal network.

Shape the Future of Computing

ACM encourages its members to take a direct hand in shaping the future of the association. There are more ways than ever to get involved.

Get Involved

Communications of the ACM (CACM) is now a fully Open Access publication.

By opening CACM to the world, we hope to increase engagement among the broader computer science community and encourage non-members to discover the rich resources ACM has to offer.

Learn More