# Publications Search Within Results Search Within Results search ## 97 results Show filters filter\_alt Sort by Year of PublicationAlphabetical A-Z sort ## 97 results Download 97 citations download- [BibTeX](/node/691221/export?format=bibtex) - [EndNote X3 XML](/node/691221/export?format=endnote8) - [EndNote 7 XML](/node/691221/export?format=endnote7) - [Endnote tagged](/node/691221/export?format=tagged) - [Marc](/node/691221/export?format=marc) - [PubMedId](/node/691221/export?format=pubmed_id) - [RIS](/node/691221/export?format=ris) ### 2022 B. Hentschel, U. Sirin, and S. Idreos, “[Entropy-Learned Hashing Constant Time Hashing with Controllable Uniformity](/publications/entropy-learned-hashing-constant-time-hashing-controllable-uniformity)”, in *ACM SIGMOD International Conference on Management of Data*, 2022. B. Hentschel, U. Sirin, and S. Idreos, “[Entropy-Learned Hashing Constant Time Hashing with Controllable Uniformity](/publications/entropy-learned-hashing-constant-time-hashing-controllable-uniformity)”, in *ACM SIGMOD International Conference on Management of Data*, 2022. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfentropylearnedhashing.pdf](/sites/g/files/omnuum4611/files/stratos/files/2022sigmod_entropylearnedhashing.pdf) Hashing is a widely used technique for creating uniformly random numbers from arbitrary input data. It is a core component in relational data systems, key-value stores, compilers, networks and many more areas used for a wide range of operations including... - [ picture\_as\_pdfentropylearnedhashing.pdf](/sites/g/files/omnuum4611/files/stratos/files/2022sigmod_entropylearnedhashing.pdf) S. Chatterjee, M. Jagadeesan, W. Qin, and S. Idreos, “[Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine](/publications/cosine-cloud-cost-optimized-self-designing-key-value-storage-engine)”, in *Proceedings of the Very Large Databases Endowment (PVLDB)*, 2022. S. Chatterjee, M. Jagadeesan, W. Qin, and S. Idreos, “[Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine](/publications/cosine-cloud-cost-optimized-self-designing-key-value-storage-engine)”, in *Proceedings of the Very Large Databases Endowment (PVLDB)*, 2022. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfcosine.pdf](/sites/g/files/omnuum4611/files/stratos/files/2022pvldb_cosine.pdf) We present a self-designing key-value storage engine, Cosine, which can always take the shape of the close to “perfect” engine architec- ture given an input workload, a cloud budget, a target performance, and required cloud SLAs. By identifying and... - [ picture\_as\_pdfcosine.pdf](/sites/g/files/omnuum4611/files/stratos/files/2022pvldb_cosine.pdf) ### 2021 K. Deeds, B. Hentschel, and S. Idreos, “[Stacked Filters: Learning to Filter by Structure](/publications/stacked-filters-learning-filter-structure)”, *Proceedings of the VLDB Endowment*, vol. 14, no. 4, pp. 600–612, 2021. K. Deeds, B. Hentschel, and S. Idreos, “[Stacked Filters: Learning to Filter by Structure](/publications/stacked-filters-learning-filter-structure)”, *Proceedings of the VLDB Endowment*, vol. 14, no. 4, pp. 600–612, 2021. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfstackedfilters\_vldb2021\_e...](/sites/g/files/omnuum4611/files/stratos/files/stackedfilters_vldb2021_extended_version.pdf) We present Stacked Filters, a new probabilistic filter which is fast and robust similar to query-agnostic filters (such as Bloom and Cuckoo filters), and at the same time brings low false positive rates and sizes similar to classifier-based filters (such... - [ picture\_as\_pdfstackedfilters\_vldb2021\_e...](/sites/g/files/omnuum4611/files/stratos/files/stackedfilters_vldb2021_extended_version.pdf) A. Wasay and S. Idreos, “[More or Less: When and How to Build Convolutional Neural Network Ensembles](/publications/more-or-less-when-and-how-build-convolutional-neural-network-ensembles)”, in *International Conference on Learning Representations (ICLR)*, 2021. A. Wasay and S. Idreos, “[More or Less: When and How to Build Convolutional Neural Network Ensembles](/publications/more-or-less-when-and-how-build-convolutional-neural-network-ensembles)”, in *International Conference on Learning Representations (ICLR)*, 2021. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfdeepcollider.pdf](/sites/g/files/omnuum4611/files/stratos/files/deepcollider.pdf) Convolutional neural networks are utilized to solve increasingly more complex problems and with more data. As a result, researchers and practitioners seek to scale the representational power of such models by adding more parameters. % However, increasing... - [ picture\_as\_pdfdeepcollider.pdf](/sites/g/files/omnuum4611/files/stratos/files/deepcollider.pdf) ### 2020 A. Wasay, B. Hentschel, Y. Liao, S. Chen, and S. Idreos, “[MOTHERNETS: RAPID DEEP ENSEMBLE LEARNING](/publications/mothernets-rapid-deep-ensemble-learning)”, in *Proceedings of the Conference on Machine Learning and Systems (MLSys)*, 2020. A. Wasay, B. Hentschel, Y. Liao, S. Chen, and S. Idreos, “[MOTHERNETS: RAPID DEEP ENSEMBLE LEARNING](/publications/mothernets-rapid-deep-ensemble-learning)”, in *Proceedings of the Conference on Machine Learning and Systems (MLSys)*, 2020. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfmothernetsmlsys2020.pdf](/sites/g/files/omnuum4611/files/stratos/files/mothernetsmlsys2020.pdf) Ensembles of deep neural networks significantly improve generalization accuracy. However, training neural network ensembles requires a large amount of computational resources and time. State-of-the-art approaches either train all networks from scratch... - [ picture\_as\_pdfmothernetsmlsys2020.pdf](/sites/g/files/omnuum4611/files/stratos/files/mothernetsmlsys2020.pdf) S. Idreos and M. Callaghan, “[Key-Value Storage Engines](/publications/key-value-storage-engines)”, in *ACM SIGMOD International Conference on Management of Data*, 2020. S. Idreos and M. Callaghan, “[Key-Value Storage Engines](/publications/key-value-storage-engines)”, in *ACM SIGMOD International Conference on Management of Data*, 2020. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfkeyvaluestorageengines.pd...](/sites/g/files/omnuum4611/files/stratos/files/keyvaluestorageengines.pdf) Key-value stores are everywhere. They power a diverse set of data-driven applications across both industry and science. Key-value stores are used as stand-alone NoSQL systems but they are also used as a part of more complex pipelines and systems such as... - [ picture\_as\_pdfkeyvaluestorageengines.pd...](/sites/g/files/omnuum4611/files/stratos/files/keyvaluestorageengines.pdf) S. Luo, S. Chatterjee, R. Ketsetsidis, N. Dayan, W. Qin, and S. Idreos, “[Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores](/publications/rosetta-robust-space-time-optimized-range-filter-key-value-stores)”, in *In Proceedings of the ACM SIGMOD International Conference on Management of Data*, 2020. S. Luo, S. Chatterjee, R. Ketsetsidis, N. Dayan, W. Qin, and S. Idreos, “[Rosetta: A Robust Space-Time Optimized Range Filter for Key-Value Stores](/publications/rosetta-robust-space-time-optimized-range-filter-key-value-stores)”, in *In Proceedings of the ACM SIGMOD International Conference on Management of Data*, 2020. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfrosetta.pdf](/sites/g/files/omnuum4611/files/stratos/files/rosetta.pdf) We introduce Rosetta, a probabilistic range filter designed specifically for LSM-tree based key-value stores. The core intuition is that we can sacrifice filter probe time because it is not visible in end-to-end key-value store performance, which in turn... - [ picture\_as\_pdfrosetta.pdf](/sites/g/files/omnuum4611/files/stratos/files/rosetta.pdf) ### 2019 S. Idreos *et al.*, “[Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn](/publications/design-continuums-and-path-toward-self-designing-key-value-stores-know-and)”, in *Biennial Conference on Innovative Data Systems Research (CIDR)*, 2019. S. Idreos *et al.*, “[Design Continuums and the Path Toward Self-Designing Key-Value Stores that Know and Learn](/publications/design-continuums-and-path-toward-self-designing-key-value-stores-know-and)”, in *Biennial Conference on Innovative Data Systems Research (CIDR)*, 2019. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfselfdesign.pdf](/sites/g/files/omnuum4611/files/stratos/files/selfdesign.pdf) We introduce the concept of design continuums for the data layout of key-value stores. A design continuum unifies major distinct data structure designs under the same model. The critical insight and potential long-term impact is that such unifying models... - [ picture\_as\_pdfselfdesign.pdf](/sites/g/files/omnuum4611/files/stratos/files/selfdesign.pdf) S. Idreos and T. Kraska, “[From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems](/publications/auto-tuning-one-size-fits-all-self-designed-and-learned-data-intensive-systems)”, in *ACM SIGMOD International Conference on Management of Data*, 2019. S. Idreos and T. Kraska, “[From Auto-tuning One Size Fits All to Self-designed and Learned Data-intensive Systems](/publications/auto-tuning-one-size-fits-all-self-designed-and-learned-data-intensive-systems)”, in *ACM SIGMOD International Conference on Management of Data*, 2019. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfselfdesignedandlearnedsys...](/sites/g/files/omnuum4611/files/stratos/files/selfdesignedandlearnedsystems.pdf) We survey new opportunities to design data systems, data structures and algorithms that can adapt to both data and query workloads. Data keeps growing, hardware keeps changing and new applications appear ever more frequently. One size does not fit all... - [ picture\_as\_pdfselfdesignedandlearnedsys...](/sites/g/files/omnuum4611/files/stratos/files/selfdesignedandlearnedsystems.pdf) N. Dayan and S. Idreos, “[The Log-Structured Merge-Bush & the Wacky Continuum](/publications/log-structured-merge-bush-wacky-continuum)”, in *ACM SIGMOD International Conference on Management of Data*, 2019. N. Dayan and S. Idreos, “[The Log-Structured Merge-Bush & the Wacky Continuum](/publications/log-structured-merge-bush-wacky-continuum)”, in *ACM SIGMOD International Conference on Management of Data*, 2019. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfwackyandthebush.pdf](/sites/g/files/omnuum4611/files/stratos/files/wackyandthebush.pdf) Data-intensive key-value stores based on the Log-Structured Merge-Tree are used in numerous modern applications ranging from social media and data science to cloud infrastructure. We show that such designs exhibit an intrinsic contention be- tween the... - [ picture\_as\_pdfwackyandthebush.pdf](/sites/g/files/omnuum4611/files/stratos/files/wackyandthebush.pdf) S. Idreos *et al.*, “[Learning Data Structure Alchemy](/publications/learning-data-structure-alchemy)”, *Bulletin of the IEEE Computer Society Technical Committee on Data Engineering*, vol. 42, no. 2, pp. 46–57, 2019. S. Idreos *et al.*, “[Learning Data Structure Alchemy](/publications/learning-data-structure-alchemy)”, *Bulletin of the IEEE Computer Society Technical Committee on Data Engineering*, vol. 42, no. 2, pp. 46–57, 2019. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdflearningdatastructurealch...](/sites/g/files/omnuum4611/files/stratos/files/learningdatastructurealchemy.pdf) We propose a solution based on first principles and AI to the decades-old problem of data structure design. Instead of working on individual designs that each can only be helpful in a small set of environments, we propose the construction of an engine, a... - [ picture\_as\_pdflearningdatastructurealch...](/sites/g/files/omnuum4611/files/stratos/files/learningdatastructurealchemy.pdf) M. Athanassoulis, K. S. Bøgh, and S. Idreos, “[Optimal Column Layout for Hybrid Workloads](/publications/optimal-column-layout-hybrid-workloads)”, *Proceedings of the Very Large Databases Endowment*, vol. 12, no. 13, 2019. M. Athanassoulis, K. S. Bøgh, and S. Idreos, “[Optimal Column Layout for Hybrid Workloads](/publications/optimal-column-layout-hybrid-workloads)”, *Proceedings of the Very Large Databases Endowment*, vol. 12, no. 13, 2019. - add\_circle\_outline do\_not\_disturb\_on Abstract - [ picture\_as\_pdfcaspervldb2020.pdf](/sites/g/files/omnuum4611/files/stratos/files/caspervldb2020.pdf) Data-intensive analytical applications need to support both efficient reads and writes. However, what is usually a good data layout for an update-heavy workload, is not well-suited for a read-mostly one and vice versa. Modern analytical data systems rely... - [ picture\_as\_pdfcaspervldb2020.pdf](/sites/g/files/omnuum4611/files/stratos/files/caspervldb2020.pdf) - Previous page chevron\_left - [1](?page=0 "Current page") - [2](?page=1 "Go to page 2") - [3](?page=2 "Go to page 3") - [ Last page 9 ](?page=8 "Go to last page") - [ Next page chevron\_right ](?page=1 "Go to next page")