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<slide-template title="Journey through the Benefits of Combining Emergent Technologies on Emergent Hardware Architectures - 5th International Conference on Communication, Computing & Industry 6.0 - 2024 (C2I6-2024), 7.12.2024" subtitle=""></slide-template>

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<title-slide titletype="C2I6-2024" title="" modulenumber="7.12.2024" subtitle="Journey through the Benefits of Combining Emergent Technologies on Emergent Hardware Architectures"></title-slide>

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# .darkblue[Jigsaw Puzzle of Research for Knowledge Graphs and the Semantic Web]

.more-more-condensed[
- "<span class="darkblue">Rules</span>" for this talk
  - Only .darkblue[very short introduction into] different .darkblue[research areas]
  - Collection of .darkblue[concrete examples of "interdisciplinary" research] results
     - <span class="darkblue">1 slide for each example</span> <span class="darkgray">(exceptional 2-3 slides) besides few slides for introduction of larger topics</span>
	 - Focus on <span class="darkblue">result and showing benefits, advantages and potential</span> of combining emergent technologies on emergent hardware architectures
     - Not much details about the how
  - Puzzle pieces for emphasizing what has been combined
  - References are provided for further reading
]

--
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      <span class="text">Puzzle</span>
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	  <span class="t"></span>
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]

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# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[Architectures of .darkblue[Emergent Hardware]]</td>
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      <span class="text">Hardware</span>
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</tbody>
</table>
]

.reference[Extended from <a target="_blank" href="http://www.imprs-dynamics.mpg.de/pdfs/Plessl_talk.pdf">C. Plessl, Accelerating Scientific Computing with Massively Parallel Computer Architectures, IMPRS Winter School, Wroclaw, 2012</a>]

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# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td width="25%"><img src="../../img/keynotes/FPGAArchitecture.svg" align="top" width="100%"></img></td>
<td>.small-font36.darkblue[LUPOSDATE on FPGA]</td>
<td>
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      <span class="text" style="padding: 0 0 0 1.2em">Query<br/>Proces-<br/>sing</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[S. Werner, Hybrid Architecture for Hardware-accelerated Query Proc. in Semantic Web DB based on Runtime Reconfigurable FPGAs, 2017]

<div class="more-more-condensed no-margin">
<table class="fullwidth">
<tr>
<td style="width:40%">
<ul class="no-margin"><li>Design Choice:<ul><li>At system deployment time generated <span class="darkblue">RMs are configured into RPs</span> at system runtime<br/><span class="darkblue">$\rightsquigarrow$ avoids long synthesis time</span></li></ul></li></ul>
</td>
<td style="width:20%"><img src="../../img/keynotes/EmptySemiStaticOperatorgraph.svg" align="top" width="100%"></img></td>
<td style="width:20%" class="textcenter">.small-font[SP<sup>2</sup>B Query 4]<br/><span class="material-icons"></span><br/>
.smaller-font.darkgray[RM: Reconfigurable Module<br/>RP: Reconfigurable Partition<br/>
SRE: Semi-static Routing Element]</td>
<td style="width:20%"><img src="../../img/keynotes/FullSemiStaticOperatorgraph.svg" align="top" width="100%"></img>
</td>
</tr>
</table>
<ul class="no-margin"><li>.darkblue[Benchmark Results]
  <ul><li>.darkblue[Reconfiguration] reduced from about half hour to few milliseconds (.darkblue[< 20 ms for all queries]) when using semi-static operator graphs</li>
  </ul></li>

<li>SP<sup>2</sup>B Benchmark
     <ul><li>Dataset sizes from 66 to 262 million triples</li>
     <li><span class="darkblue bold">Speedups between 4 and 32 times</span> <span class="darkgray">(dependent on query and dataset size)</span></li>
	 </ul>
  </li>
</ul></li>
</ul>
</div>

---
# .smaller-little-font.no-margin[
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<td>.small-font36[Timeline of .darkblue[Quantum Computers]]</td>
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</table>
]
.reference[<a href="https://en.wikipedia.org/wiki/List_of_quantum_processors" target="_blank">Main Data Source</a> Roadmaps: <a href="https://research.ibm.com/blog/ibm-quantum-roadmap" target="_blank">IBM</a> <a href="https://www.computerwoche.de/a/2025-kommt-der-erste-4-000-qubit-quantencomputer" target="_blank">IBM'25</a> <a href="https://quantumai.google/research" target="_blank">Google</a> <a href="https://doi.org/10.1007/s42354-021-0342-8" target="_blank">Quantum Brilliance</a> <a href="https://www.dwavesys.com/media/xvjpraig/clarity-roadmap_digital_v2.pdf" target="_blank">D-Wave</a> <a href="https://education.jlab.org/qa/mathatom_05.html" target="_blank">#Atoms on Earth</a> <a href="https://www.popularmechanics.com/space/a27259/how-many-particles-are-in-the-entire-universe/" target="_blank">#Particles in Universe</a>]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
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<td>.small-font36[Using .darkblue[Hardware Accelerator] for optimizing Queries / Transaction Schedules]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
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      <span class="text">QC</span>
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</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
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	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[<a href="https://doi.org/10.1145/3410566.3410593" target="_blank">[BG20a]</a> <a href="http://nbn-resolving.de/urn:nbn:de:101:1-2020112218332015343957" target="_blank">[BG20b]</a>  <a href="https://doi.org/10.1145/3472163.3472164" target="_blank">[GG21]</a>]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
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<td>.small-font36[Approaches for .darkblue[Query/Transaction Schedule Optimization]]</td>
<td>
<div class="jigsaw1">
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      <span class="text">QC</span>
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	  <span class="b"></span>
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      <span class="text">DB</span>
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</td>
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</table>
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[.darkblue[Transaction Scheduling]]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
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</td>
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]

.reference[<a href="https://doi.org/10.14778/3598581.3598603" target="_blank">VLDB'23</a>]

.more-more-condensed.no-margin[
- Design choice: .darkblue[Fast quantum] exact methods .darkblue[and] good .darkblue[heuristics] for scalable optimization times
]
<table class="no-margin">
<tbody>
<tr>
<td>
.more-more-condensed.no-margin[
- .darkblue[Classical]
  - .darkblue[Dynamic Programming | Random Search | Simulated Annealing .darkgray[(SA)] | Genetic Algorithm .darkgray[(GA)] | Ant Colony Optimization .darkgray[(ACO)]]
- .darkblue[Quantum]
  - .darkblue[Maximum Independent Set | Quantum Evolutionary Algorithms] .darkgray[(QEA) (several variants: Binary | (Cultural (QCEA)) Multi-State)] .darkblue[| QUBO | Grover]
]
</td>
<td style="width:55%;vertical-align:top">
<div class="textcenter">
<img src="../../img/qc4db/TS_QEA.png" align="top" class="fullwidth"></img>
</div>
</td>
</tr>
</tbody>
</table>

---
# .smaller-little-font.no-margin[
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<td>.small-font36[.darkblue[Transaction Scheduling] via .darkblue[Quantum Annealing]]</td>
<td>
<div class="jigsaw1">
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	  <span class="r"></span>
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      <span class="text">QC</span>
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]

.reference[<a href="https://doi.org/10.1145/3410566.3410593" target="_blank">T. Bittner, S. Groppe, Avoiding Blocking by Scheduling Transactions using Quantum Annealing, IDEAS 2020</a>]

.more-more-condensed.no-margin[
<table>
<tr>
<td style="width:65%;vertical-align:top">
.more-more-condensed.no-margin[
- Design Choice: Using .darkblue[Quantum Annealing for constant execution time]
- Experiments on real .darkblue[Quantum Annealer] (D-Wave 2000Q cloud service)
  - first minute free<br/>(afterwards too much for our budget)
- Versus .darkblue[Simulated Annealing on CPU]
- .darkblue[Preprocessing time/Number of QuBits]: $O((n\cdot k\cdot R)^2)$
]
</td><td style="width:35%;vertical-align:top">
<div class="textcenter"><img src="../../img/keynotes/ExperimentsQuantumAnnealing.svg" align="top" width="100%" class="padding-bottom"></img></div>
</td></tr>
</table>
<div class="textcenter no-margin"><img src="../../img/keynotes/ExperimentsQuantumAnnealingTable.svg" align="top" width="100%"></img><div class="smaller-font">$k$: #cores, $n$: #transactions, $R$: max. makespan, $O$: conflict matrix, $l_x$: transaction length, $r_x = R-l_x$</div></div>  
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[.darkblue[QML] for .darkblue[Join Ordering]]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">QC</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[Single-Step: <a href="https://doi.org/10.1145/3579142.3594299" target="_blank">BiDEDE@SIGMOD'23</a> <a href="https://doi.org/10.14778/3598581.3598603" target="_blank">VLDB'23</a> <a href="https://doi.org/10.1145/3555041.3589404" target="_blank">SIGMOD'23</a> Multi-Step (cooperation with OTH Regensburg): <a href="https://doi.org/10.48550/arXiv.2405.07770" target="_blank">QCE'24</a>]

.more-more-condensed.no-margin[
- Design choice: .darkblue[Predict best join order based on] real .darkblue[execution times] .darkgray[in contrast to estimated costs (EC)]
  - can .darkblue[beat exact methods] like dynamic programming (DP) based on EC
- Single-Step: .darkblue[Real-world queries] .darkgray[(ErgastF1 Benchmark/PostgreSQL)]
  - .darkblue[join orders with faster execution than DP (2.8%) and GEQO (11.2%)]
  - close to classical ML like RTOS (6%) / best join orders (16.8%)
]
<table>
<tbody>
<tr>
<td style="width:50%">
<div class="textcenter">
<img src="../../img/keynotes/bv/SingleVsMultiStepJoinOrdering.svg" align="top" style="width:95%"></img>
</div>
</td>
<td style="width:50%">
<div class="textcenter">
<img src="../../img/keynotes/bv/QMLJOO.svg" align="top" style="width:95%"></img>
</div>
</td>
</tr>
</tbody>
</table>

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[.darkblue[Join Ordering as QUBO Problem]]</td>
<td>
<div class="jigsaw1">
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	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">QC</span>
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<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[<a href="https://doi.org/10.1145/3579142.3594298" target="_blank">BiDEDE@SIGMOD'23</a> Partitioning the Search Space: <a href="https://doi.org/10.1007/s13222-024-00468-3" target="_blank">Datenbank-Spektrum 24(1): 21-32, 2024</a>]

.more-more-condensed.no-margin[
- Design choice: .darkblue[Direct Join Costs] .darkgray[in contrast to calculated costs]
  - .darkblue[Estimated costs for .bold[each] join]  .darkblue[$\Rightarrow$ better join order]
  - .darkblue[Runtime] complexity .darkblue[$O(2^m-m)$ with $m$ relations] to join .darkblue[optimal] for join ordering based on direct costs
     - <span class="darkblue">Classical</span> runtime: <span class="darkblue">$O(3^m-2^{m+1})$</span> using dynamic programming
- .darkblue[Real-world queries] .darkgray[(ErgastF1 Benchmark/PostgreSQL)]
]
<table>
<tbody>
<tr>
<td style="width:55%">
<div class="textcenter">
<img src="../../img/keynotes/bv/QAResults.svg" align="top" style="width:95%"></img>
</div>
</td>
<td style="width:45%">
<div class="textcenter">
<img src="../../img/keynotes/bv/QAResultsSelectivity.svg" align="top" style="width:95%"></img>
</div>
</td>
</tr>
</tbody>
</table>

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[.darkblue[QML for Cardinality Estimation]]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">QC</span>
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<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
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</tbody>
</table>
]

.reference[MSCN (multi-set convolutional network): <a href="http://cidrdb.org/cidr2019/papers/p101-kipf-cidr19.pdf" target="_blank">Kipf et al. Learned cardinalities: Estimating correlated joins with deep learning, CIDR 2019</a>]

.more-more-condensed.no-margin[
- Design choice: .darkblue[Predict correction factor] of a Cardinality Estimator
  - .darkblue[Cardinality Predictor:PostgreSQL|MSCN|Hybrid Quantum Classical Network]
  - .darkblue[Cardinality Correction: Hybrid Quantum Classical Network]
- Queries of JOB-light Benchmark
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[.darkblue[Transaction Scheduling Optimisation Server]]</td>
<td>
<div class="jigsaw1">
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	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">QC</span>
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<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
</tr>
</tbody>
</table>
]

<img src="../../img/qc4db/TSOS.svg" align="top" class="fullwidth"></img>
.more-more-condensed[
- .darkblue[Demonstrator: fully quantum .bold[versus] fully classical]
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[Demo for .darkblue[Query/Transaction Schedule Opt./Exec. Time Prediction]]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
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	  <span class="b"></span>
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      <span class="text">QC</span>
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	  <span class="t"></span>
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	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[Link to demo: <a href="https://qc4db.quantum-brilliance.com/" target="_blank">https://qc4db.quantum-brilliance.com/</a>]

.more-more-condensed[
- .darkblue[is publicly available] at the .darkblue[Quantum Brilliance web-page]...<br/>
<video class="fullwidth" controls>
  <source src="../../img/qc4db/quantum_demo.mp4" type="video/mp4">
Your browser does not support the video tag.
</video>
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[.darkblue[Hybrid Multi-Model Multi-Platform (HM3P) Database]]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Multi-<br/>Model</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 0 0 0 1.2em">Multi-<br/>Plat-<br/>form</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[S. Groppe, J. Groppe, Hybrid Multi-Model Multi-Platform (HM3P) Databases, DATA 2020.]

.more-condensed[
<img src="../../img/keynotes/HM3PDatabase.svg" align="top" width="100%"></img>
<span class="darkgreenbackground filledCircle">+</span> full and uniform .darkgreen[data integration] at database level<br/>
<span class="darkgreenbackground filledCircle">+</span> .darkgreen[performance]: fully optimized across different data models<br/>
<span class="darkgreenbackground filledCircle">+</span> transparent .darkgreen[fault-tolerance]<br/>
<span class="darkgreenbackground filledCircle">+</span> SQL .darkgreen[standards]: .small-font22[relational ('87), XML ('03), temporal ('11), JSON ('16), Multi-dimensional Arrays ('19), schemaless ('19), streams ('20?), property graphs ('21?)]<br/>
<span class="darkgreenbackground filledCircle">+</span> <span class="bold">.darkgreen[features of different] types of .darkgreen[databases running on different platforms] can be used
</span>]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[Variant: .darkblue[Semantic] HM3P (.darkblue[S]HM3P) DB]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Seman-<br/>tic</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Multi-<br/>Model</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 0 0 0 1.2em">Multi-<br/>Plat-<br/>form</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
</tr>
</tbody>
</table>
]
.reference[S. Groppe, Semantic Hybrid Multi-Model Multi-Platform (SHM3P) Databases, ISIC 2021.]

.more-condensed.no-padding.no-margin[
<img src="../../img/keynotes/SHM3P.svg" align="top" width="100%"></img>
<ul>
<li><span class="darkblue">Semantic Layer as glue</span> between other models and platforms</li>
<li><span class="darkblue">new challenges</span> like integrating different types of reasoners in a transparent global reasoner</li>
</ul>
<table>
<tbody>
<tr>
<td style="width:55%;vertical-align:top">
<span class="darkgreenbackground filledCircle">+</span> .darkgreen[Features of HM3P] databases<br/>
<span class="darkgreenbackground filledCircle">+</span> Easier .darkgreen[data integration]<br/>
</td>
<td style="width:45%;vertical-align:top">
<span class="darkredbackground filledCircle">-</span> .darkblue[Performance issues] may occur due to semantic layer
</td>
</tr>
</tbody>
</table>
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[The Power of .darkblue[Multi-Platform]: .darkblue[LUPOSDATE3000]]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Seman-<br/>tic</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 0 0 0 1.2em">Multi-<br/>Plat-<br/>form</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
</tr>
</tbody>
</table>
]
.more-more-condensed.no-margin[
<ul><li><span class="darkblue">LUPOSDATE300: Open Source Multi-Platform SW database</span>
<ul><li>Design Choice: Implemented in Kotlin for multi-platform support</li></ul></li></ul>
<table>
<tbody>
<tr>
<td class="halfwidth" style="vertical-align:top">
.more-more-condensed[
<ul><ul><li>.darkblue[ultra-fast in jvm]...
  <div class="textcenter" style="line-height:0.6;"><img src="../../img/keynotes/LUPOSDATE3000Experiments.svg" align="top" class="fullwidth"></img><br/>.smaller-font[<a href="https://doi.org/10.18420/btw2021-12" target="_blank">B. Warnke, M.W. Rehan, S. Fischer, S. Groppe: Flexible data partitioning schemes for parallel merge joins in semantic web queries in: BTW'21</a>]</div></li></ul>
]
</td>
<td class="halfwidth" style="vertical-align:top">
.more-more-condensed[
<ul><ul><li>...but also .darkblue[enabling web demos running completely in] the .darkblue[browser!]
    <div class="textcenter" style="line-height:0.6;"><video controls="controls" preload="preload" class="fullwidth">
  <source src="https://www.ifis.uni-luebeck.de/~groppe/soundofdatabases/resources/CommonSubexpressions.mp4">
  Your browser does not support the video element!
</video><br/><span class="smaller-font"><a href="http://www.vldb.org/pvldb/vol14/p2695-groppe.pdf" target="_blank">S. Groppe, R. Klinckenberg, B. Warnke. Sound of Databases: Sonification of a Semantic Web Database Engine. PVLDB, 14(12), 2021</a></span></div></li></ul>
]
</td>
</tr>
</tbody>
</table>
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36[.darkblue[Query Opt.] via .darkblue[Rein-<br/>forcement Learning]]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Seman-<br/>tic</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 0 0 0 1.2em">Multi-<br/>Plat-<br/>form</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">ML</span>
</div>
</td>
</tr>
</tbody>
</table>
]
.reference[<a href="https://doi.org/10.3390/bdcc8070071" target="_blank">Warnke, Martens, Winker, Groppe, Adhiyaman, Srinivasan, Shridevi, ReJOOSp: Reinforcement learning for join order optimization in SPARQL, BDCC, 8(7)</a>]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.small-font36.darkblue[Query Opt. & Routing in IoT]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Seman-<br/>tic</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 0 0 0 1.2em">Multi-<br/>Plat-<br/>form</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">DB</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">IoT</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Query<br/>Opt.</span>
</div>
</td>
</tr>
</tbody>
</table>
]
.reference[<a href="https://doi.org/10.1145/3589462.3589497" target="_blank">Warnke, Fischer, Groppe. ReJOOSp: Distributed SPARQL Queries in Collaboration with the Routing Protocol, IDEAS 2023</a>]

.more-more-condensed.no-margin[
- Design Choice: .darkblue[Combining Routing and Query Optimization] in IoT
  - Q9 with 11 triple patterns: <span class="darkblue">Network traffic reduces by</span>
     - <span class="darkblue">routing-assisted join order</span> (instead of centralized query optimization)
	 - <span class="darkblue">dynamic relocation of operators</span> whenever the result of a join is more significant than its inputs (4% for large and 23% for small data scales)
]
<div class="textcenter no-margin">
<img src="../../img/keynotes/IoT/IoTRoutingOptQ9.svg" align="top" width="65%"></img>
</div>
<div class="smaller-font no-margin darkgray" style="line-height:0.9">Transferred data for benchmark queries in percent (smaller $\rightsquigarrow$ better) dependent on option. More significant effects are in the center of the circle.</div>

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.darkblue.small-font36[Interplay of LLMs and KGs]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">LLM</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">KG</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[<a href="https://vldb.org/workshops/2024/proceedings/LLM+KG/LLM+KG-9.pdf" target="_blank">Khorashadizadeh et al., Research Trends for the Interplay between Large Language Models and Knowledge Graphs, LLM+KG@VLDB'24</a>, <a href="https://doi.org/10.48550/arxiv.2406.08223" target="_blank">ArXiv version</a>]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.darkblue.small-font36[KG Construction with LLMs]<br/>.small-font26[(Project with Uni Paris/Toulouse)]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">LLM</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 0 0 0 1.2em">Con-<br/>struct<br/>KG</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[<sup>*</sup> <a href="https://doi.org/10.48550/arXiv.2305.08804" target="_blank">H.Khorashadizadeh et al., Exploring In-Context Learning Capabilities of Foundation Models for Generating KGs from Text, Text2KG@ESWC 2023</a>]

.more-more-condensed.no-margin[
<div class="textcenter"><img src="../../img/keynotes/bv/KGExtractionFromLLM.svg" width="100%"/></div>
- .darkblue[Knowledge graph (KG) construction, completion, fact validation, extraction facts from unseen text]<sup>*</sup> (limitations due to hallucination)
]

.reference[<a href="https://doi.org/10.1007/978-3-031-70239-6_31" target="_blank">Ezzabady et al., Towards Generating High-Quality Knowledge Graphs by Leveraging Large Language Models, NLDB 2024</a>]

.more-more-condensed.no-margin[
<div class="textcenter"><img src="../../img/keynotes/bv/KGConstructionWithLLMs.svg" style="width:80%"/></div>
<ul><li>.darkblue[Result] (comparison to manual labeling)<div class="margin-bottom"><table class="result small-font tablesmallpadding">
<thead>
<tr class="header">
<th style="text-align: center;"></th>
<th style="text-align: center;"><span class="smallcaps">Cov</span></th>
<th style="text-align: center;"><span class="smallcaps">Gen</span></th>
<th style="text-align: center;"><span class="smallcaps">Db</span></th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td style="text-align: center;"><strong>Macro F1-score</strong></td>
<td style="text-align: center;">66.60</td>
<td style="text-align: center;"><strong>76.93</strong></td>
<td style="text-align: center;">72.85</td>
</tr>
</tbody>
</table></div>
<ul><li><span class="darkblue">COV:</span> Represents the original triples extracted <span class="darkblue">using GPT-4</span></li>
<li><span class="darkblue">GEN:</span> COV with <span class="darkblue">augmented data generated by GPT-4</span></li>
<li><span class="darkblue">DB:</span> Merges the original triples from <span class="darkblue">COV with triples from an external data source</span> (with daily statistical updates and policy actions)</li></ul>
</li></ul>
]

.reference[Khorashadizadeh et al. Construction and Canonicalization of Economic Knowledge Graphs with LLMs, KGSWC 2024]

<table>
<tbody>
<tr>
<td style="width:40%">
.more-more-condensed.no-margin[
- Khorashadizadeh et al. .darkblue[Construction and Canonicalization of Economic Knowledge Graphs with LLMs], KGSWC 2024.
  - Approach to .bold.darkblue[Open Information Extraction]
     - extracting structured information directly from unstructured text <span class="bold darkblue">without a predefined schema</span>
  - .bold.darkblue[Presentation:] Friday,<br/>13.12.2024,<br/>13:15 – 13:40
]
</td>
<td style="width:60%">
<div class="textcenter more-more-condensed bold">
.darkblue[Pipeline:]<br/>
<img src="../../img/keynotes/covid-19/OpenIE_Pipeline.svg" align="top" class="fullwidth"></img>
</div>
</td>
</tbody>
</table>

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.darkblue.small-font36[KG for Analysis of COVID-19 Policies] .darkgray.small-font[(Policy Importance in 183 Nations)]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">KG</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">COVID-<br/>19</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">ML</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[Khorashadizadeh et al., Assessing the Impact of Government Policies on COVID-19 Spread: A Machine Learning Approach, LOD2024]

.more-more-condensed[
<div class="textcenter">
<img src="../../img/keynotes/bv/total_coef_RF_new_cases_per_million.svg" align="top" style="width:92%"></img>
</div>
]

.reference[Khorashadizadeh et al., Assessing the Impact of Government Policies on COVID-19 Spread: A Machine Learning Approach, LOD2024]

.more-more-condensed[
<div class="textcenter">
<img src="../../img/keynotes/bv/total_coef_RF_new_deaths_per_million.svg" align="top" style="width:92%"></img>
</div>
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.darkblue.small-font36[Nations clustered by COVID-19 Policies Importance]<br/>.darkgray.small-font[(Policy Importance in 183 Nations)]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">KG</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">COVID-<br/>19</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">ML</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[Khorashadizadeh et al., Assessing the Impact of Government Policies on COVID-19 Spread: A Machine Learning Approach, LOD2024]

.more-more-condensed[
<div class="textcenter">
<img src="../../img/keynotes/bv/WorldClusteredByPolicyImpact.svg" align="top" style="width:100%"></img>
</div>
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.darkblue.small-font36[LLM Applications]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">LLM</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Appli-<br/>cation</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[LangChain/LangGraph | AutoGen | ...]

<table class="padding-top" style="border-collapse:collapse">
<tbody><tr>
<td style="width:70%;vertical-align:top;">
<div class="more-more-condensed no-margin"><ul>
<li>New kind of application in form of a network of (LLM, human, code) agents
<ul>
<li><span class="darkblue">Applying LLMs as new paradigm for software development</span> of applications close to natural language/human users</li>
<li>Development of <span class="darkblue">new LLM applications</span> like
<ul><li>modern chatbots</li><li><span class="darkblue">extraction from natural languages text</span> (e.g., for knowledge graph construction)</li><li><span class="darkblue">new types of applications with reasoning capabilities</span> (self-reflection etc. for human-like generation of content)</li></ul></li>
</ul>
</li>
</ul>
</div>
</td>
<td style="width:30%;vertical-align:top;">
<img src="../../img/keynotes/ConversableAgents.svg" class="fullwidth">
</td>
</tr>
<tr>
</tr>
</tbody></table>

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.darkblue.small-font36[Citizen Science Platforms for Biodiversity]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">Bio</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Citizen<br/>Science</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[<a href="https://lesherbonautes.mnhn.fr/" target="_blank">https://lesherbonautes.mnhn.fr/</a>]

.more-more-condensed.small-font24[
- launched by .darkblue[Muséum national d'Histoire naturelle (MNHN)]/Paris
- collaborative way of contributing to the .darkblue[creation of a scientific database based on] the .darkblue[millions of photos of plants] in the national network of naturalist collections (Récolnat)
<div class="textcenter">
<img src="../../img/keynotes/bv/LesHerbonautes.jpg" align="top" style="width:75%" class="margin-bottom"></img>
</div>
]

---
# .smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>.darkblue.small-font36[LLMs4Citizen Science] .small-font.darkgray[(Les Herbonautes, project with MNHN/Uni Paris)]</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">Bio</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Citizen<br/>Science</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">LLM</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Appli-<br/>cation</span>
</div>
</td>
</tr>
</tbody>
</table>
]

.reference[Kessel et al., Impact of Chatbots on User Experience and Data Quality in Citizen Science Platforms. submitted.]

<table class="padding-top" style="border-collapse:collapse">
<tr>
<td style="width:70%;vertical-align:top;">
.more-more-condensed.no-margin[
- LLM in form of a network of (LLM, human, code) agents as basis for an .darkblue[easy-to-use chatbot]
  - .darkblue[for guiding] users .darkblue[through] the .darkblue[data validation] process and .darkblue[with which users can] retrieve important information by .darkblue[posing related questions] for adding missing information (as some kind of easy-to-use tool for their research) 
  - .darkblue[for high-level analysis] with visual presentation of the analysis results in the form of maps, charts and/or in text
]
</td>
<td style="width:30%;vertical-align:top;">
<img src="../../img/keynotes/bv/ConversibleAgents.svg" class="fullwidth"></img>
</td>
</tr>
<tr>
<td colspan="2">
.more-more-condensed.no-margin[
<ul><ul><li>.darkblue[for detecting anomalies and marking any wrong data], or data and results of the chatbot they do not trust, as a trigger for data validation</li>
<li>.darkblue[for multi-lingual support]</li>
</ul></ul>
]
</td>
</tr>
</table>

.reference[Kessel et al., Impact of Chatbots on User Experience and Data Quality in Citizen Science Platforms. submitted.]

.reference[Kessel et al., Impact of Chatbots on User Experience and Data Quality in Citizen Science Platforms. submitted.]

---
# .darkblue[What's next to combine?]

.smaller-little-font.no-margin[
<table class="no-margin">
<tbody>
<tr>
<td><span class="small-font bold darkblue">Data Model:</span></td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">SW</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">KG</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Rela-<br/>tional</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">XML</span>
</div>
</td>
</tr>
<tr>
<td><span class="small-font bold darkblue">Hardware Acceleration:</span></td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Many-Core</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">GPU</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">FPGA</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">QC</span>
</div>
</td>
<td style="padding-left:2em">.small-font32[...]</td>
</tr>
<tr>
<td><span class="small-font bold darkblue">Technologies:</span></td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">LLM</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 0 0 0 1.2em">Symb.<br/>Reaso-<br/>ning</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Clas-<br/>sical ML</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 0 0 0 1.2em">Query<br/>Proces-<br/>sing</span>
</div>
</td>
</tr>
<tr>
<td><span class="small-font bold darkblue">Execution Environment:</span></td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">Cloud</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">Parallel Server</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">IoT</span>
</div>
</td>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">Mobile</span>
</div>
</td>
</tr>
<tr>
<td></td><td></td>
<td class="textcenter">.small-font32[...]</td>
</tr>
</tbody>
</table>
]

---
# .darkblue[Are we going to be replaced?<br/>Can the job been done by AI?]

.reference[C. Lu, C. Lu, R.T. Lange, J. Foerster, J. Clune, D. Ha: The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery, <a href="https://doi.org/10.48550/arXiv.2408.06292" target="_blank">arXiv:2408.06292</a>, 2024.]

.more-more-condensed[
- .darkblue[The AI Scientist:]
]
.smaller-little-font[
<div class="mermaid">
graph LR
		subgraph Paper Write-Up
			PW1(Manu-<br/>script<br/>Tem-<br/>plate) --> PW2(Text<br/>via<br/>LLM<br/>and<br/>aider)
			PW2 --> PW3(Manu-<br/>script)
			PW3 --> PW4(LLM<br/>Paper<br/>Re-<br/>view-<br/>ing)
		end
		subgraph Experiment Iteration
			EI1(Exper-<br/>iment<br/>Tem-<br/>plate) --> EI2(Code<br/>via<br/>LLM<br/>and<br/>aider)
			EI2 --> EI3(Experi-<br/>ments<br/><-><br/>Update<br/>Plan)
			EI3 --> EI4(Nume-<br/>rical/<br/>Data<br/>Plots)
		end
		subgraph Idea Generation
			IG1(LLM<br/>Idea/<br/>Inno-<br/>vation<br/>Plan) --> IG2(Novelty<br/>Check<br/>Semantic<br/>Scholar)
			IG2 --> IG3(Idea<br/>Sco-<br/>ring/<br/>Ar-<br/>chiv-<br/>ing)
		end
		EI4 --> PW1
		IG3 --> EI1
		PW4 -.-> IG1
</div>
]

--
count: false
.smaller-little-font.no-margin[
<table>
<tbody>
<tr>
<td>
<div class="jigsaw1">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text">LLM</span>
</div>
</td>
<td>
<div class="jigsaw2">
	  <span class="t"></span>
	  <span class="r"></span>
	  <span class="b"></span>
	  <span class="l"></span>
      <span class="text" style="padding: 1em 0 0 1.2em">PhD student</span>
</div>
</td>
</tr>
</tbody>
</table>
]

---
# .darkblue[Quantum and Intelligent Computing (.bold[QIC])]
.more-more-condensed.no-margin[
- .darkblue[Head:] Prof. Dr. rer. nat. habil. <span class="darkblue">Sven Groppe</span>
- .darkblue[Projects and Research Assistants]
]
.more-more-condensed.no-margin[
<table>
<tbody>
<tr>
<td class="halfwidth" style="vertical-align:top">
<ul><ul><li>.darkblue.bold[QC4DB]: Accelerating Relational Database Management Systems via Quantum Computing (BMBF)
 <ul><li><span class="lila">Umut Çalikyilmaz</span></li>
 <li><span class="lila">Tobias Winker</span></li>
 <li><span class="lila">Nitin Nayak</span></li>
 </ul></li>
<li>.darkblue.bold[QualityOnt]: High Quality Knowledge Graphs from recent English, French and German Emergent Trends with the example of COVID-19 (DFG/ANR)
 <ul><li><span class="lila">Hanieh Khorashadizadeh</span></li>
 </ul></li> 
<li>.darkblue.bold[Semantic Data] Integration and Analysis (Bosch)
 <ul><li><span class="lila">Simon Paasche</span> (External PhD Student)</li>
 </ul></li> 
</ul></ul> 
</td>
<td class="halfwidth" style="vertical-align:top">
<ul><ul><li>.darkblue.bold[Hybrid$^2$]-Index Structures for Main Memory Databasess (DFG)
 <ul><li><span class="lila">Tobias Groth</span></li>
 </ul></li> 
<li>.darkblue.bold[BigSIoT]: Big Data Management für das Semantic IoT (DFG)
 <ul><li><span class="lila">Dr. Benjamin Warnke</span></li>
 </ul></li> 
<li>.darkblue.bold[SW-FPGA-DB]: Hardwarebeschleunigung von SW DBs durch dynamisch rekonfig. FPGAs (DFG)
 <ul><li><span class="lila">Dr. Dennis Heinrich</span></li>
 </ul></li> 
<li>.darkblue.bold[FPGA-Cluster DB]: Beschleunigung rel. DBs mittels laufzeitadaptiver FPGA Cluster (ZIM-BMWi)
 <ul><li><span class="lila">Dr. Stefan Werner</span></li>
 </ul></li> 
</ul></ul> 
</td>
</tr>
</tbody>
</table>
]