One of the most overlooked business trends affecting intranets has been the delivery of real-time key performance indicators and customer information. Executives are asking for precise, real time information and see it as a necessary tool to make smart business decisions. As a recent Harvard Business Review article discussed, depending upon the nature of your organization, this real-time information need can take the form of real time customer transaction information, progress reports on product developments, productivity metrics, subscriber lists and cash flow. Delivery of select real-time information is not difficult given the right technology tools, the hard question to determine will be what information is really needed in real-time and for whom and how should it be delivered.
https://commerce.net/mindystaging/wp-content/uploads/2021/09/commercenet-logo-1.png00amshttps://commerce.net/mindystaging/wp-content/uploads/2021/09/commercenet-logo-1.pngams2004-11-18 02:10:052004-11-18 02:10:05Real Time in Intranets
The problem addressed by RealNames – that is the poverty of the DNS as a naming and navigation system for the world’s internet users – remains unresolved.
Matt Welsh, Harvard University Title: Market-Based Programming Paradigms for Sensor Networks Date: 10/20/2004 Time: 2:00 -3:30 PM Location: Hawthorne 1S-F40 Host: Fred Douglis
Abstract:
Sensor networks present a novel programming challenge: that of achieving robust global behavior despite limited resources, varying node locations and capabilities, and changing network conditions. Current programming models typically require that global behavior be specified in terms of the low-level actions of individual nodes. This approach makes it extremely difficult to tune the operation of the sensor network as a whole.Ideally, sensor nodes should self-schedule to determine the set of operations that maximizes that node’s contribution to the network-wide task. In this talk, we present market-based macroprogramming (MBM), a new approach for achieving efficient resource allocation in sensor networks. Rather than programming individual sensor nodes, MBM defines a virtual market in which nodes sell goods (such as sensor readings or data aggregates) in response to prices that are established by the programmer. Nodes take actions to maximize their profit, subject to energy budget constraints. The behavior of the network is determined by adjusting the price vectors for each good, rather than by directly specifying local node programs. Nodes individually specialize their operation in response to feedback from payments. Market-based macroprogramming provides a useful set of primitives for controlling the aggregate behavior of sensor networks despite variance of individual nodes. We present the MBM paradigm and a sensor network vehicle tracking application based on this design, as well as a number of experiments demonstrating that MBM allows nodes to operate efficiently under limited energy budgets, while adapting to changing network conditions. This project is in collaboration with Geoff Mainland and David Parkes.
Biography:
Matt Welsh is an assistant professor of Computer Science at Harvard University. Prior to joining Harvard, he received his Ph.D. from UC Berkeley, and spent one year as a visiting researcher at Intel Research Berkeley. His research interests span many aspects of complex systems, including Internet services, distributed systems, and sensor networks.
P2P clusters like the Grid and PlanetLab enable in principle the same statistical multiplexing efficiency gains for computing as the Internet provides for networking. The key unsolved problem is resource allocation. Existing solutions are not economically efficient and require high latency to acquire resources. We designed and implemented Tycoon, a market based distributed resource allocation system based on an Auction Share scheduling algorithm. Preliminary results show that Tycoon achieves low latency and high fairness while providing incentives for truth-telling on the part of strategic users.
Tony Gentile pointed eBay’s move to release pricing data as a Web service:
eBay announced the availability of Pulse, a tool that aggregates bids to provide up-to-the-minute (and historical) values for goods (and some services).
Yes, that’s right… eBay, who, unlike your local newspaper, has transactions that clear (i.e., you know that the item was sold and how much it was sold for), knows the fair market price, globally, of millions of different goods… and they’ve just opened it up for mining!
Many implications:
1) We’ve seen the high-water mark for The Kelley Blue Book’s value (and similar companies). Through Pulse, eBay Motors can be mined to provide near real-time and historical pricing information, on any make or model, in a narrow geographic region (i.e., local search), with car photos documenting the condition of the car, etc. Nice.
2) Data availability will impact marketplace participant behavior, likely resulting in Meta-marketplaces, ‘day-sellers’ and increased competition. For example, much as NexTag.com built a meta-marketplace by arbitraging SEM marketplaces (Google, Overture, etc) until finally finding a profitable niche in home mortgages, the ability to monitor demand on eBay for a particular good or service may result in speculative and opportunistic seller behavior, resulting in more (and more immediate) competition in eBay’s marketplace.
Interestingly, just as with Overture/Google, tools that make the marketplace more efficient, as described above, may have a negative short-term impact on revenue (as anything that decreases the avg selling price of an item on eBay would), but will likely result in significantly greater long-term impact as the increased transparency leads to greater trust, allowing more transactions to move online.
3) eBay continues to ensure its Web 2.0 relevance by extending its existing services 1: Classified Listings and Transaction clearing (core marketplace), 2: Payments (via PayPal), and 3: Reputation (core marketplace), by adding its first data product service, Pricing.
Real Time in Intranets
Event Driven ArchitecturesLine56.com: Intranet Trends to Watch For:
Real Names
DecentralizationJohn Battelle points out that RealNames has relaunched. Founder Keith Teare writes:
Market-based Macroprogramming
CommerceIBM Research – Distributed and Fault Tolerant Computing – Current Seminars at Watson
Matt Welsh, Harvard University Title: Market-Based Programming Paradigms for Sensor Networks Date: 10/20/2004 Time: 2:00 -3:30 PM Location: Hawthorne 1S-F40 Host: Fred Douglis
Abstract:
Sensor networks present a novel programming challenge: that of achieving robust global behavior despite limited resources, varying node locations and capabilities, and changing network conditions. Current programming models typically require that global behavior be specified in terms of the low-level actions of individual nodes. This approach makes it extremely difficult to tune the operation of the sensor network as a whole.Ideally, sensor nodes should self-schedule to determine the set of operations that maximizes that node’s contribution to the network-wide task. In this talk, we present market-based macroprogramming (MBM), a new approach for achieving efficient resource allocation in sensor networks. Rather than programming individual sensor nodes, MBM defines a virtual market in which nodes sell goods (such as sensor readings or data aggregates) in response to prices that are established by the programmer. Nodes take actions to maximize their profit, subject to energy budget constraints. The behavior of the network is determined by adjusting the price vectors for each good, rather than by directly specifying local node programs. Nodes individually specialize their operation in response to feedback from payments. Market-based macroprogramming provides a useful set of primitives for controlling the aggregate behavior of sensor networks despite variance of individual nodes. We present the MBM paradigm and a sensor network vehicle tracking application based on this design, as well as a number of experiments demonstrating that MBM allows nodes to operate efficiently under limited energy budgets, while adapting to changing network conditions. This project is in collaboration with Geoff Mainland and David Parkes.
Biography:
Matt Welsh is an assistant professor of Computer Science at Harvard University. Prior to joining Harvard, he received his Ph.D. from UC Berkeley, and spent one year as a visiting researcher at Intel Research Berkeley. His research interests span many aspects of complex systems, including Internet services, distributed systems, and sensor networks.
Market Based Resource Allocation
DecentralizationFine is a former student of Ledyard’s, who also Hanson’s advisor.
Research at HP Labs : Information Dynamics Lab : Papers : Eliminating Public Knowledge Biases in Small Group Predictions
Kevin Lai, Bernardo A. Huberman, Leslie R. Fine
HP Laboratories
Palo Alto, CA 94304
Abstract
P2P clusters like the Grid and PlanetLab enable in principle the same statistical multiplexing efficiency gains for computing as the Internet provides for networking. The key unsolved problem is resource allocation. Existing solutions are not economically efficient and require high latency to acquire resources. We designed and implemented Tycoon, a market based distributed resource allocation system based on an Auction Share scheduling algorithm. Preliminary results show that Tycoon achieves low latency and high fairness while providing incentives for truth-telling on the part of strategic users.
eBay as Pricing Authority
CommerceTony Gentile pointed eBay’s move to release pricing data as a Web service: