RISMA: A Rule-based Interval State Machine Algorithm for Performance Analysis, Alerts Generation, and Monitoring Real-Time Data Processing S. Laban 1 and A.I. El-Desouky 2 Science and Technology 2013 Conference Vienna, Austria. 1 Processing Systems Officer – Preparatory Commission for the Comprehensive Nuclear- Test-Ban Treaty (CTBT) Organization, Vienna International Centre, P.O. Box 1200, A-1400, Vienna, Austria. E-Mail: shaban.laban@ctbto.org 2 Professor - Computer and Systems Department, Faculty of Engineering, Mansoura University, Egypt.
Outline � Motivation � Background � RISMA: A Rule-based Interval State Machine Algorithm for Performance Analysis, Alerts Generation, and Monitoring Real-Time Data Processing � Interval state machine (ISM) � State transition diagram and relationships � Algorithm rules and priorities � Implementation � Results and analysis � Algorithm derived information • Alerts • Operation performance indicator • Timeliness • Data availability � SHI examples � RN examples � Conclusion and future work Page 2
Motivation The monitoring of real-time systems is a challenging and complicated process. � The monitored data and their properties are rapidly, not necessarily linear, and continuously changing with time. � Need to detect anomalous behaviors of monitored systems and determine/understand the sequence in which they occurred. � Produce necessary and timely alerts during the workflow monitoring of such systems. � Need to identify and use Key Performance Indicators (KPI). � Interval-based theorems can be used for monitoring interval-based data processing. However, increasing the number of non-linear intervals makes the implementation of such theorems a complex and time consuming process. � knowledge-based systems are fast and efficient and used for monitoring different systems. However, implementation with real-time data is not straight forward . � Can we use knowledge-based systems with interval-based theorems in monitoring interval-based data t? IDC Page 3
Background The interval-based or period-based theorems have been discussed, analyzed, and used by many researches in Artificial Intelligence (AI), philosophy, and linguistics. As explained by Allen, there are 13 relations between any two intervals. However, processing a very large number of similar non-ordered intervals with classical interval-based theorems is a complicated and time consuming process. This is due to the need to infer an unlimited number of relationships between these intervals. The most popular inference engines used in artificial intelligence community is based on the C Language Integrated Production System (CLIPS). CLIPS shell is a forward-chaining rule-based tool which was originally developed at NASA's Johnson Space Centre and is based on the Rete algorithm. The inference engine of CLIPS is characterized by its explanation facility that provides valuable information to the user about how the inference engine arrived at its conclusions. IDC Page 4
Interval State Machine (ISM) The proposed ISM approach is used to model any interval-based data. The algorithm identifies four main different states for any similar non-ordered intervals as follows: NEW, LATEST, OLD, and DELETED. The Latest Moving Interval Approach As the received intervals are not necessarily ordered. The algorithm is designed to maintain and make sure that the LATEST state is always having the maximum ending time using its internal necessary rules. This can be achieved by applying the following constraints and assumptions: 1. The processed intervals are not necessarily linearly ordered intervals. 2. There will be one and only one LATEST interval. 3. The LATEST interval should have the maximum end time for all the existing permanent intervals. 4. There could be as many as of OLD intervals or none. 5. The primary state of any received interval is NEW and the algorithm will sequentially process/infer them. 6. There will be one temporary NEW interval. 7. After inferring the NEW interval, it should not exist anymore and will be converted to either OLD, LATEST, or DELETED. 8. The DELETED intervals are removed from memory. IDC Page 5
State transition diagram IDC Page 6
States relationships 18 direct relationships (Stations & Channels) NEW-LATEST relationships � • 11 relationships OLD-LATEST relationships � • 1 relationship OLD-OLD relationships � • 6 relationships Alert-Alert relationships (Stations) The meets relationship is used to • concatenate similar alerts. The equals relationships is mostly • used to end any onGoing alerts or complement the missing information of the onGoing alerts. IDC Page 7
Algorithm rules and priorities Begin RISMA For Each NEW Interval /* handling alerts */ Rules Priority ……. /* manipulating and sorting the NEW intervals */ IF not exits LATEST then generate-alert-incomplete-information-for-NEW-interval 1 Create LATEST with NEW Information. IF NEW after LATEST then alert-incomplete-information-ended 2 LATEST information = NEW information and Delete NEW. alert-meets-alert IF NEW equals or during LATEST then 3 Delete NEW. alert-complements-alert IF NEW overlaps or meets LTEST then 4 Begin time of LATEST = begin time of NEW and Delete NEW. new-after-latest IF NEW starts LATEST then new-during-latest End time of LATEST = maximum end time of both and Delete NEW. IF NEW finishes LATEST then new-equals-latest Begin time of LATEST = minimum begin time of both and Delete NEW. new-overlaps-latest IF LATEST after NEW then 5 NEW becomes OLD. new-meets-latest IF LATEST during NEW then new-ends-latest Begin and end times of LATEST equal begin and end times of NEW respectively and Delete OLD. new-starts-latest IF LATEST overlaps or meets NEW then End time of LATEST = end time of NEW and Delete NEW. IF OLD meets LATEST then new-before-latest Begin time of LATEST = begin time of OLD and Delete NEW. new-contains-latest /* manipulating and sorting the OLD intervals */ IF OLD i equals or during OLD j then 6 new-overlapped_by-latest Delete OLD i . newt-met_by-latest IF OLD i overlaps or meets OLD j then Begin time of OLD j = begin time of OLD i and Delete OLD i . IF OLD i starts OLD j then old-meets-latest 7 End time of OLD j = maximum end time of both and Delete OLD i . old-during-old IF OLD i finishes OLD j then Begin time of OLD j = minimum begin time of both and Delete OLD i . old-equals-old Finally: old-overlaps-old /* generate missing data alerts */ 8 IF OLD max precedes LATEST then old-starts-old Generate missing data alert with begin time=end time of OLD max and end old-ends-old time = begin time of LATEST. IF OLD i precedes OLD j then Generate missing data alert with begin time=end time of OLD i and end time old-meets-old 9 = begin time of OLD j . End RISMA generate-alert-missing-data 10 IDC Page 8
Recommend
More recommend
