1. Extracting Task Information
from Past Process Execution Logs
Indentify User Tasks from Past Usage Logs
ISTI-CNR (CNR)
Franco Maria Nardini, Gabriele Tolomei, CNR

2. Learning Package Categorization
S-Cube
Monitoring and Analysis of SBA
Task Modeling
Extracting Task Information
from Past Process Execution Logs

3. Connections to the S-Cube IRF
 Conceptual Research Framework:
– Service Composition and Coordination
– Service Infrastructure
– Adaptation and Monitoring
 Logical Run-Time Architecture:
– Monitoring Engine
– Adaptation Engine
– Negotiation Engine
– Runtime QA Engine
– Resource Broker
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4. Overview
 Introduction
 Goal
 Methodology
 Experiments
 Conclusions

5. Background Concepts: Usage Logs
 Most complex software systems collect their lifecycle
usage data in log files:
– Web search engines store a tremendous amount of data about
their users in query logs:
- e.g., issued queries, timestamps, clicked results, etc.
– SBS event logs contain several information about service
components exchanging messages
- e.g., service invocation, service failure, registry querying, etc.
 Usage logs represent a huge source of “hidden”
information (i.e., knowledge)
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6. Knowledge Discovery from Usage Logs
 Data Mining algorithms and techniques allow extracting
valuable knowledge from usage logs
 Extracted knowledge may refer to several aspects:
– e.g., finding usage patterns, modeling user behavior, etc.
 If properly exploited, such knowledge might help
improving the overall quality of the system
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7. The Web as a Task-Execution Platform
 Activities people perform are usually composition of
atomic tasks
 The accomplishment of those activities is moving
towards the Web platform
 Examples:
– planning a travel (overused!)
– organizing a birthday party
– getting a U.S. visa
– etc.
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8. Overview
 Introduction
 Goal
 Methodology
 Experiments
 Conclusions

9. Goal
 Re-construct tasks/processes that users perform on the
Web by means of issued queries to search engines:
– i.e., mining Web-mediated tasks from past issued user queries
 Extracting tasks/processes from historical search data
(i.e., query logs) collected by Web search engines
 Task-based Session Discovery Problem: approached
using clustering-based techniques
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10. Overview
 Introduction
 Goal
 Methodology
 Experiments
 Conclusions

11. Query Log Mining
 Idea: cluster queries in a way that queries in the same cluster
are likely to be task-related
 Input: stream of queries issued by one user
 Output: set of clusters of queries representing search tasks
for that user
 Key points:
– features (e.g., lexical content, time, semantic, etc.)
– clustering algorithm (e.g., centroid-based, density-based, novel
heuristics)
– distance metrics (e,g., Jaccard, Levenstein, cosine, etc.)
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12. Our Solution
 A graph-based heuristics for discovering queries that are
related to the same search task
 Our technique has proven to outperform state-of-the-art
approaches
 Results was presented in a research paper published at the
4th ACM Conference on Web Search and Data Mining
(WSDM 2011)
– Identifying Task-based Sessions in Search Engine Query Logs
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13. Overview
 Introduction
 Goal
 Methodology
 Experiments
 Conclusions

14. Data Set: 2006 AOL Query Log
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15. Evaluation
 We manually extract a set of tasks from a portion of our
testing query log (i.e., ground-truth)
 We run our proposed algorithm and evaluate its accuracy in
discovering the manually-labeled tasks of the ground-truth
 Evaluation is expressed in terms of popular IR-based metrics:
– Precision
– Recall
– F-measure (i.e., harmonic mean of Precision and Recall)
– Rand
– Jaccard
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16. Results
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17. Implications for SBS domain: Why?
 Our technique was thought for, but not limited to Web search
context
 Service-based Systems could be another suitable context of
application
 Tasks might be single service instances
 Processes might be workflows of orchestrated services
 Query/Task clustering can be considered as a special case of
more general “activity clustering”
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18. Implications for SBS domain: How?
 Past usage log data are the key point for applying our
technique
 Once we have logs of performed activities (e.g., service
invocations) we can figure out features
 Then we can cluster activities according to those features on
a task/process-based perspective
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19. Overview
 Introduction
 Goal
 Methodology
 Experiments
 Conclusions

20. Conclusions
 We developed a technique for mining tasks/processes from
Web search logs
 Our technique is based on clustering historical search data
according to some features
 This approach might be generalized and applied to several
other contexts (e.g., software-based services)
 We need usage logs from which we can extract suitable
features and common interfaces!