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<title>Just-in-Time Data Structures</title>
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Exploring <u><b>O</b></u>nline <u><b>D</b></u>ata <u><b>In</b></u>teractions
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<h2>Just-in-Time Data Structures</h2>
<h4>Oliver Kennedy</h4>
<h4><a href="mailto:okennedy@buffalo.edu">okennedy@buffalo.edu</a></h4>
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<table>
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<td width="150px"><img src="graphics/saurav.jpg" height="150px"/></td>
<td width="150px"><img src="graphics/darshana.jpg" height="150px"/></td>
<td width="150px"><img src="graphics/hank.jpg" height="150px"/></td>
<td width="150px"><img src="graphics/ankur.jpg" height="150px"/></td>
<td width="150px"><img src="graphics/luke.png" height="150px"/></td>
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<tr style="font-size: smaller;">
<td>Saurav Singhi</td>
<td>Darshana Balakrishnan<br/</td>
<td>Hank Lin</td>
<td>Ankur Upadhyay</td>
<td>Lukasz Ziarek</td>
</tr>
<tr style="font-size: small;">
<td>(PhD In Progress)</td>
<td>(MS In Progress)</td>
<td>(BS 2017)</td>
<td>(MS 2014)</td>
<td>(Asst. Prof @ UB)</td>
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<section>
<!-- Establish the setting:
- Data Structures are a game of trade-offs.
- Which structure is best? Well, it depends!
- Specific data structures lock you in to a specific set of tradeoffs:
- Read vs Write
- Batch vs Individual Updates
- Scan vs Lookup vs Range Queries
-->
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<!-- Get deeper in to the problem:
- Dynamic workloads: (E.g., Data Loading -> Data access || Regional Differences in Access Patterns)
- State of the art:
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- Option 2: Design a data structure *specifically* for your transitional needs (e.g., LSM trees)
- What would it take to allow a data structure to incrementally transition from one set of tradeoffs to another one?
- Challenge: We have no way to work with, or even to describe such an "intermediate" data structure in the middle of transitioning
- Talk outline:
- A Universal Data Structure Instance description language
- Accessing and Modifying Static Data Structures
- Optimizing Static Data Structures
- Dynamic Data
- Policy Discovery and Optimization
-->
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<section>
<!-- A Universal Data Structure Instance description language
- Initial goal: Describing an instance of a data structure at one specific point in time.
- Approach: Commonalities between different data structures... standard patterns
- Records
- Pointers (to a address of a physical entity that describes a collection of records)
- Semantics (explicit or implicit properties of the physical layout that can improve queries over the ds)
- Specifics: Specific building blocks
- [X] U [Y]
- Record
- Generalizations:
- Repetition (requires array notation)
- Semantic Extensions (Binary Tree, Hash Table, Sorted Array)
- Language (CFG for data structure instances)
- Examples (borrow from paper?)
-->
</section>
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<!-- Accessing Static Data Structures
- Queries as morphisms on the language
- Enumeration + Pop
- Lookups
- Scans
-->
<!-- Dynamic Data
- Do the minimum work possible: Linked List
-->
</section>
<section>
<!-- Optimizing Static Data Structures
- Pattern + Replacement Language
- Events
- Examples
- Cracker Index
- Splay Tree
- Lazy BTree
-->
</section>
<section>
<!-- Policy Discovery and Optimization
- Purely Heuristic Design: Cracker Index / Transition Policy / Splay Trees
- Assisted Discovery: Parameterized Search Space + Cost Model
- Autonomous Discovery:
- Simulation-Based
- (Likely Expensive) Chase-style
-->
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