Distribution System for Discount Sales
ABSTRACT
There is a growing need for systems that react automatically to events. While some events are generated externally and deliver data across distributed systems, others need to be derived by the system itself based on available information. Event derivation is hampered by uncertainty attributed to causes such as unreliable data sources or the inability to determine with certainty whether an event has actually occurred, given available information. Two main challenges exist when designing a solution for event derivation under uncertainty. First, event derivation should scale under heavy loads of incoming events. Second, the associated probabilities must be correctly captured and represented. We present a solution to both problems by introducing a novel generic and formal mechanism and framework for managing event derivation under uncertainty. We also provide empirical evidence demonstrating the scalability and accuracy of our approach.
Existing System
Some events are generated externally and deliver data across distributed systems, while other events and their related data need to be derived by the system itself, based on other events and some derivation mechanism. In many cases, such derivation is carried out based on a set of rules (e.g., rules in active databases and special purpose event derivation rule languages such as the Situation Manager Rule Language ). Carrying out such event derivation is hampered by the gap between the actual occurrences of events, to which the system must respond, and the ability of event-driven systems to accurately generate events. This gap results in uncertainty and may be attributed to unreliable event sources (e.g., an inaccurate sensor reading or an unreliable Web service), an unreliable network (e.g., packet loss at routers), or the inability to determine with certainty whether a phenomenon has actually occurred given the available information sources. Therefore, a clear trade-off exists between deriving events with certainty, using full and complete information, and the need to provide a quick notification of newly revealed events. Both responding to a threat without sufficient evidence and waiting too long to respond may have undesirable consequences.
Proposed System
One way of managing the gap between actual events and event notifications is to explicitly handle uncertainty. This could be done by modeling events uncertainty as a probability associated with each event, whether such events are generated externally or derived. However, a major challenge in such explicit management of events’ uncertainty is that rule-based systems need to process multiple rules with multiple event sources. Correctly calculating event probabilities while taking into account various types of uncertainty is not trivial. Clearly, correct quantification of the probability of derived events serves as an important tool for decision making. Event generation under uncertainty should therefore be accompanied with an appropriate mechanism for probability computation.
MODULE DESCRIPTION:
1. Event Model
2. Derivation Model
3. Probability Space Definition
4. Selectability
Modules Description
1. Event Model
An event is an actual occurrence or happening that is significant (falls within a domain of discourse), and atomic (it either occurs or not). This definition, while limited, suits our specific needs. Examples of events include termination of workflow activity, daily OtCCMS, and a person entering a certain geographical area. We differentiate between two types of events. Explicit events are signaled by external event sources (e.g., OtCCMS events). Derived events are events for which no direct signal exists, but rather need to be derived based on other events, e.g., Flu Outbreak and Anthrax Attack events.
2. Derivation Model
Derived events in our model are inferred using rules. For ease of exposition, we refrain from presenting complete rule language syntax. Rather, we represent a rule by a quintuple, r ¼ hsr; pr; ar;mr; prri defining the necessary conditions for the derivation of new events. Such a quintuple can be implemented in a variety of ways, such as a set of XQuery statements, Horn clauses, and CPTs such as in, or as a set of procedural statements. “If there is an increase in OtCCMS for four sequential days to a total increase of 350, then the probability of a flu outbreak is 90 percent.” Recall that OtCCMS events contain the volume of the daily sales.
3. Probability Space Definition
A major novelty of our framework is in the support of the calculation of probabilities associated with derived events, at a given time point t. At time t, the set of possible derived events is determined by the explicit EIDs known at t (together with the defined rules). Therefore, since different sets of explicit EIDs may be available at different time points, a (possibly) different probability space needs to be defined for each time point separately.
4. Selectability
Selectability, as defined by function sr in a rule specification, plays an important role in event derivation, in both the deterministic and the uncertain settings. First, it defines which events are relevant to derivation according to rule r—an important semantic distinction. Just by analyzing the definition of sr it is clear to a human which events are defined as being relevant to derivation according to r, and which events are ignored in this derivation. Selectability significantly influences the performance of the inference algorithm.
System Configuration:-
H/W System Configuration:-
Procesor - Pentium –III
Speed - 1.1 Ghz
RAM - 256 MB (min)
Hard Disk - 20 GB
Floppy Drive - 1.44 MB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - SVGA
S/W System Configuration:-
v Operating System :Windows95/98/2000/XP
v Application Server : Tomcat5.0/6.X
v Front End : HTML, Java, Jsp
v Scripts : JavaScript.
v Server side Script : Java Server Pages.
v Database : Mysql
v Database Connectivity : JDBC.
CONCLUSION
In this work, we presented an efficient mechanism for event derivation under uncertainty. A model for representing derived events was presented together with a Monte Carlo sampling algorithm that approximates the derived event probabilities. We experimented with the sampling algorithm, showing it to be comparable to the performance of a deterministic event composition system. It is scalable under an increasing number of possible worlds (and uncertain rules), while a Bayesian network algorithm for the same purpose does not scale well, as it is exponential in the states of the events as was described. Finally, the sampling algorithm provides an accurate estimation of probabilities. Our contribution can be summarized as follows: The introduction of a novel generic and formal mechanism and framework for managing and deriving events under uncertainty conditions.
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