Abstract
Aside from the conception of new blockchain architectures, existing blockchain optimizations in the literature primarily focus on system or data-oriented optimizations within prevailing blockchains. However, since blockchains handle multiple aspects ranging from organizational governance to smart contract design, a holistic approach that encompasses all the different layers of a given blockchain system is required to ensure that all optimization opportunities are taken into consideration. In this vein, we define a multi-level optimization recommendation approach that identifies optimization opportunities within a blockchain at the system, data, and user level. Multiple metrics and attributes are derived from a blockchain log and nine optimization recommendations are formalized. We implement an automated optimization recommendation tool, BlockOptR, based on these concepts. The system is extensively evaluated with a wide range of workloads covering multiple real-world scenarios. After implementing the recommended optimizations, we observe an average of 20% improvement in the success rate of transactions and an average of 40% improvement in latency.
Supplemental Material
Available for Download
Read me
Source Code
- Parinaz Ameri. 2016. Challenges of index recommendation for databases: With specific evaluation on a NoSQL database. In dalam 28th GI-Workshop on Foundations of Databases (Grundlagen von Datenbaken), Nörten-Hardenberg, Germany.Google Scholar
- Parinaz Ameri. 2016. On a self-tuning index recommendation approach for databases. In 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW). 201--205. https://doi.org/10.1109/ICDEW.2016.7495648Google ScholarCross Ref
- Mohammad Javad Amiri, Divyakant Agrawal, and Amr El Abbadi. 2021. Permissioned Blockchains: Properties, Techniques and Applications. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3448016.3457539Google ScholarDigital Library
- Analyzing the complaints process at Granada city council 2020. https://www.tf-pm.org/resources/casestudy/analyzing-the-complains-prociess-at-granada-city-council.pdf. [Online; accessed 12-April-2023].Google Scholar
- Elli Androulaki, Artem Barger, Vita Bortnikov, Christian Cachin, Konstantinos Christidis, Angelo De Caro, David Enyeart, Christopher Ferris, Gennady Laventman, Yacov Manevich, Srinivasan Muralidharan, Chet Murthy, Binh Nguyen, Manish Sethi, Gari Singh, Keith Smith, Alessandro Sorniotti, Chrysoula Stathakopoulou, Marko Vukolic, Sharon Weed Cocco, and Jason Yellick. 2018. Hyperledger Fabric: A Distributed Operating System for Permissioned Blockchains. In Proceedings of the Thirteenth EuroSys Conference (Porto, Portugal) (EuroSys '18). ACM, New York, NY, USA, Article 30, 15 pages. https://doi.org/10.1145/3190508.3190538Google ScholarDigital Library
- Arati Baliga, Nitesh Solanki, Shubham Verekar, Amol Pednekar, Pandurang Kamat, and Siddhartha Chatterjee. 2018. Performance Characterization of Hyperledger Fabric. In 2018 Crypto Valley Conference on Blockchain Technology (CVCBT). 65--74. https://doi.org/10.1109/CVCBT.2018.00013Google ScholarCross Ref
- Arati Baliga, I Subhod, Pandurang Kamat, and Siddhartha Chatterjee. 2018. Performance Evaluation of the Quorum Blockchain Platform. https://doi.org/10.48550/ARXIV.1809.03421Google ScholarCross Ref
- Dina Bayomie, Iman Helal, Ahmed Awad, Ehab Ezat, and Ali Elbastawissi. 2015. Deducing Case IDs for Unlabeled Event Logs, Vol. 256. https://doi.org/10.1007/978--3--319--42887--1_20Google ScholarCross Ref
- Sara Bergman, Mikael Asplund, and Simin Nadjm-Tehrani. 2020. Permissioned blockchains and distributed databases: A performance study. Concurrency and Computation: Practice and Experience 32, 12 (2020), e5227. https://doi.org/10. 1002/cpe.5227 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.5227 e5227 cpe.5227.Google ScholarCross Ref
- BlockOptR 2022. https://github.com/jeetachacko/BlockOptR. [Online; accessed 12-April-2023].Google Scholar
- Diego Calvanese, Giuseppe De Giacomo, and Marco Montali. 2013. Foundations of Data-Aware Process Analysis: A Database Theory Perspective. In Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (New York, New York, USA) (PODS '13). Association for Computing Machinery, New York, NY, USA, 1--12. https://doi.org/10.1145/2463664.2467796Google ScholarDigital Library
- Celonis Process Mining 2022. https://www.celonis.com/. [Online; accessed 12-April-2023].Google Scholar
- Jeeta Ann Chacko, Ruben Mayer, and Hans-Arno Jacobsen. 2021. Why Do My Blockchain Transactions Fail? A Study of Hyperledger Fabric. In Proceedings of the 2021 International Conference on Management of Data (Virtual Event, China) (SIGMOD/PODS '21). Association for Computing Machinery, New York, NY, USA, 221--234. https://doi.org/10.1145/3448016.3452823Google ScholarDigital Library
- Gloria Chatzopoulou, Magdalini Eirinaki, and Neoklis Polyzotis. 2009. Query Recommendations for Interactive Database Exploration. In Scientific and Statistical Database Management, Marianne Winslett (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 3--18.Google Scholar
- Surajit Chaudhuri and Vivek Narasayya. 2007. Self-Tuning Database Systems: A Decade of Progress. In Proceedings of the 33rd International Conference on Very Large Data Bases (Vienna, Austria) (VLDB '07). VLDB Endowment, 3--14.Google Scholar
- Mohammad Jabed Morshed Chowdhury, Alan Colman, Muhammad Ashad Kabir, Jun Han, and Paul Sarda. 2018. Blockchain Versus Database: A Critical Analysis. In 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE). 1348--1353. https://doi.org/10.1109/TrustCom/BigDataSE.2018.00186Google ScholarCross Ref
- Combining Multiple Columns as Case ID 2020. https://fluxicon.com/book/read/perspectives/#combining-multiple-columns-as-case-id. [Online; accessed 12-April-2023].Google Scholar
- Corda 2022. https://docs.r3.com/en/platform/corda/4.10/enterprise/key-concepts-notaries.html. [Online; accessed 12-April-2023].Google Scholar
- Data Requirements: Case ID 2020. https://fluxicon.com/book/read/dataext/#case-id. [Online; accessed 12-April-2023].Google Scholar
- Daniel Deutch and Tova Milo. 2011. A Quest for Beauty and Wealth (or, Business Processes for Database Researchers). In Proceedings of the Thirtieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (Athens, Greece) (PODS '11). Association for Computing Machinery, New York, NY, USA, 1--12. https://doi.org/10.1145/1989284.1989286Google ScholarDigital Library
- Claudio Di Ciccio, Alessio Cecconi, Marlon Dumas, Luciano García-Bañuelos, Orlenys López-Pintado, Qinghua Lu, Jan Mendling, Alexander Ponomarev, An Binh Tran, and Ingo Weber. 2019. Blockchain support for collaborative business processes. Informatik Spektrum 42, 3 (2019), 182--190.Google ScholarCross Ref
- Tien Tuan Anh Dinh, Rui Liu, Meihui Zhang, Gang Chen, Beng Chin Ooi, and Ji Wang. 2018. Untangling Blockchain: A Data Processing View of Blockchain Systems. IEEE Transactions on Knowledge and Data Engineering 30, 7 (2018), 1366--1385. https://doi.org/10.1109/TKDE.2017.2781227Google ScholarCross Ref
- Julian Dreyer, Marten Fischer, and Ralf Tönjes. 2020. Performance Analysis of Hyperledger Fabric 2.0 Blockchain Platform. In Proceedings of the Workshop on Cloud Continuum Services for Smart IoT Systems (Virtual Event, Japan) (CCIoT '20). Association for Computing Machinery, New York, NY, USA, 32--38. https://doi.org/10.1145/3417310.3431398Google ScholarDigital Library
- Frank Duchmann and Agnes Koschmider. 2019. Validation of smart contracts using process mining. In ZEUS. CEUR workshop proceedings, Vol. 2339. 13--16.Google Scholar
- FabricSharp Git Repository 2022. https://github.com/ooibc88/FabricSharp. [Online; accessed 12-April-2023].Google Scholar
- Ghareeb Falazi, Vikas Khinchi, Uwe Breitenbücher, and Frank Leymann. 2019. Transactional properties of permissioned blockchains. SICS Software-Intensive Cyber-Physical Systems (2019). https://doi.org/10.1007/s00450-019-00411-yGoogle ScholarCross Ref
- Christian Gorenflo, Stephen Lee, Lukasz Golab, and Srinivasan Keshav. 2019. FastFabric: Scaling Hyperledger Fabric to 20,000 Transactions per Second. In 2019 IEEE International Conference on Blockchain and Cryptocurrency (ICBC). 455--463. https://doi.org/10.1109/BLOC.2019.8751452Google ScholarCross Ref
- Gideon Greenspan et al . 2015. Multichain private blockchain-white paper. URl: http://www. multichain.com/download/MultiChain-White-Paper. pdf (2015), 57--60.Google Scholar
- Christian W Günther and Anne Rozinat. 2012. Disco: Discover Your Processes. BPM (Demos) 940 (2012), 40--44.Google Scholar
- Christian W Günther and Wil MP Van Der Aalst. 2007. Fuzzy mining--adaptive process simplification based on multi-perspective metrics. In International conference on business process management. Springer, 328--343.Google ScholarCross Ref
- Theo Härder. 1984. Observations on optimistic concurrency control schemes. Information Systems 9, 2 (1984), 111 -- 120. https://doi.org/10.1016/0306--4379(84)90020--6Google ScholarDigital Library
- Richard Hobeck, Christopher Klinkmüller, Hmn Dilum Bandara, Ingo Weber, and Wil Van der Aalst. 2021. Process Mining on Blockchain Data: a Case Study of Augur. Technical Report. EasyChair.Google Scholar
- Christian Hugo Hoffmann. 2021. Blockchain Use Cases Revisited: Micro-Lending Solutions for Retail Banking and Financial Inclusion. Journal of Systems Science and Information 9, 1 (2021), 1--15. https://doi.org/doi:10.21078/JSSI-2021-001--15Google ScholarCross Ref
- Richard Hull. 2008. Artifact-Centric Business Process Models: Brief Survey of Research Results and Challenges. In On the Move to Meaningful Internet Systems: OTM 2008, Robert Meersman and Zahir Tari (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 1152--1163.Google ScholarDigital Library
- Hyperledger Caliper 2020. https://hyperledger.github.io/caliper/. [Online; accessed 12-April-2023].Google Scholar
- Zsolt István, Alessandro Sorniotti, and Marko Vukolic. 2018. Streamchain: Do blockchains need blocks?. In Proceedings of the 2nd Workshop on Scalable and Resilient Infrastructures for Distributed Ledgers. 1--6.Google ScholarDigital Library
- Haris Javaid, Chengchen Hu, and Gordon Brebner. 2019. Optimizing Validation Phase of Hyperledger Fabric. In 2019 IEEE 27th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS). 269--275. https://doi.org/10.1109/MASCOTS.2019.00038Google ScholarCross Ref
- Sadhana J. Kamatkar, Ajit Kamble, Amelec Viloria, Lissette Hernández-Fernandez, and Ernesto García Cali. 2018. Database Performance Tuning and Query Optimization. In Data Mining and Big Data, Ying Tan, Yuhui Shi, and Qirong Tang (Eds.). Springer International Publishing, Cham, 3--11.Google Scholar
- Christopher Klinkmüller, Alexander Ponomarev, An Binh Tran, Ingo Weber, and Wil van der Aalst. 2019. Mining blockchain processes: Extracting process mining data from blockchain applications. In International Conference on Business Process Management. Springer, 71--86.Google Scholar
- Olga Labazova, Erol Kazan, Tobias Dehling, Tuure Tuunanen, and Ali Sunyaev. 2021. Managing Blockchain Systems and Applications: A Process Model for Blockchain Configurations. arXiv preprint arXiv:2105.02118 (2021).Google Scholar
- Mengting Liu, F. Richard Yu, Yinglei Teng, Victor C. M. Leung, and Mei Song. 2019. Performance Optimization for Blockchain-Enabled Industrial Internet of Things (IIoT) Systems: A Deep Reinforcement Learning Approach. IEEE Transactions on Industrial Informatics 15, 6 (2019), 3559--3570. https://doi.org/10.1109/TII.2019.2897805Google ScholarCross Ref
- Jiaheng Lu, Yuxing Chen, Herodotos Herodotou, and Shivnath Babu. 2019. Speedup Your Analytics: Automatic Parameter Tuning for Databases and Big Data Systems. Proc. VLDB Endow. 12, 12 (aug 2019). https://doi.org/10.14778/3352063.3352112Google ScholarDigital Library
- Orlenys López-Pintado, Luciano García-Bañuelos, Marlon Dumas, Ingo Weber, and Alex Ponomarev. 2018. CATERPILLAR: A Business Process Execution Engine on the Ethereum Blockchain. https://doi.org/10.48550/ARXIV.1808.03517Google ScholarCross Ref
- Heidy M. Marin-Castro and Edgar Tello-Leal. 2021. Event Log Preprocessing for Process Mining: A Review. Applied Sciences 11, 22 (2021). https://doi.org/10.3390/app112210556Google ScholarCross Ref
- Dennis McLeod and John Miles Smith. 1980. Abstraction in Databases. In Proceedings of the 1980 Workshop on Data Abstraction, Databases and Conceptual Modeling (Pingree Park, Colorado, USA). Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/800227.806871Google ScholarDigital Library
- Du Mingxiao, Ma Xiaofeng, Zhang Zhe, Wang Xiangwei, and Chen Qijun. 2017. A review on consensus algorithm of blockchain. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2567--2572. https://doi.org/10.1109/SMC.2017.8123011Google ScholarDigital Library
- Bhabendu Kumar Mohanta, Soumyashree S Panda, and Debasish Jena. 2018. An Overview of Smart Contract and Use Cases in Blockchain Technology. In 2018 9th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 1--4. https://doi.org/10.1109/ICCCNT.2018.8494045Google ScholarCross Ref
- JP Morgan. 2016. Quorum whitepaper. New York: JP Morgan Chase (2016).Google Scholar
- Roman Mühlberger, Stefan Bachhofner, Claudio Di Ciccio, Luciano García-Bañuelos, and Orlenys López-Pintado. 2019. Extracting event logs for process mining from data stored on the blockchain. In International Conference on Business Process Management. Springer, 690--703.Google ScholarCross Ref
- Satoshi Nakamoto. 2008. Bitcoin: A peer-to-peer electronic cash system. Decentralized Business Review (2008), 21260.Google Scholar
- Q. Nasir, Ilham A. Qasse, M. Talib, and A. B. Nassif. 2018. Performance Analysis of Hyperledger Fabric Platforms. Secur. Commun. Networks 2018 (2018), 3976093:1--3976093:14.Google Scholar
- Pezhman Nasirifard, Ruben Mayer, and Hans-Arno Jacobsen. 2019. FabricCRDT: A Conflict-Free Replicated Datatypes Approach to Permissioned Blockchains. In Proceedings of the 20th International Middleware Conference (Davis, CA, USA) (Middleware '19). Association for Computing Machinery, New York, NY, USA, 110-122. https://doi.org/10.1145/3361525.3361540Google ScholarDigital Library
- Senthil Nathan, Chander Govindarajan, Adarsh Saraf, Manish Sethi, and Praveen Jayachandran. 2019. Blockchain Meets Database: Design and Implementation of a Blockchain Relational Database. Proc. VLDB Endow. 12, 11 (jul 2019). https://doi.org/10.14778/3342263.3342632Google ScholarDigital Library
- Keerthi Nelaturu, Sidi Mohamed Beillahi, Fan Long, and Andreas Veneris. 2021. Smart Contracts Refinement for Gas Optimization. In 2021 3rd Conference on Blockchain Research Applications for Innovative Networks and Services (BRAINS). 229--236. https://doi.org/10.1109/BRAINS52497.2021.9569819Google ScholarCross Ref
- Diego Ongaro and John Ousterhout. 2014. In Search of an Understandable Consensus Algorithm. In Proceedings of the 2014 USENIX Conference on USENIX Annual Technical Conference (Philadelphia, PA) (USENIX ATC'14). USENIX Association, Berkeley, CA, USA, 305--320. http://dl.acm.org/citation.cfm?id=2643634.2643666Google Scholar
- Orlenys Pintado, Luciano García-Bañuelos, Marlon Dumas, Ingo Weber, and Alexander Ponomarev. 2019. Caterpillar: A business process execution engine on the Ethereum blockchain. Software: Practice and Experience (05 2019). https://doi.org/10.1002/spe.2702Google ScholarCross Ref
- Process mining on the loan application process of a Dutch Financial Institute 2017. https://www.win.tue.nl/bpi/2017/bpi2017_winner_professional.pdf. [Online; accessed 12-April-2023].Google Scholar
- Yuncheng Qiao, Chaoqun Ma, Qiujun Lan, and Zhongding Zhou. 2019/12. Inventory Financing Model Based on Blockchain Technology. In Proceedings of the Fourth International Conference on Economic and Business Management (FEBM 2019). Atlantis Press, 337--342. https://doi.org/10.2991/febm-19.2019.7Google ScholarCross Ref
- Mayank Raikwar, Danilo Gligoroski, and Goran Velinov. 2020. Trends in Development of Databases and Blockchain. In 2020 Seventh International Conference on Software Defined Systems (SDS). 177--182. https://doi.org/10.1109/SDS49854.2020.9143893Google ScholarCross Ref
- Aravind Ramachandran and Dr. Murat Kantarcioglu. 2017. Using Blockchain and smart contracts for secure data provenance management. arXiv (2017).Google Scholar
- Michel Rauchs, Apolline Blandin, Keith Bear, and Stephen B McKeon. 2019. 2nd global enterprise blockchain bench- marking study. Available at SSRN 3461765 (2019).Google Scholar
- Olivier Rikken, Marijn Janssen, and Zenlin Kwee. 2019. Governance challenges of blockchain and decentralized autonomous organizations. Information Polity 24 (11 2019), 1--21. https://doi.org/10.3233/IP-190154Google ScholarDigital Library
- Henrique Rocha and Stéphane Ducasse. 2018. Preliminary Steps Towards Modeling Blockchain Oriented Software. In 2018 IEEE/ACM 1st International Workshop on Emerging Trends in Software Engineering for Blockchain (WETSEB). 52--57.Google Scholar
- Pingcheng Ruan, Tien Tuan Anh Dinh, Dumitrel Loghin, Meihui Zhang, Gang Chen, Qian Lin, and Beng Chin Ooi. 2021. Blockchains vs. Distributed Databases: Dichotomy and Fusion. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3448016.3452789Google ScholarDigital Library
- Pingcheng Ruan, Dumitrel Loghin, Quang-Trung Ta, Meihui Zhang, Gang Chen, and Beng Chin Ooi. 2020. A Transactional Perspective on Execute-Order-Validate Blockchains. In Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data (Portland, OR, USA) (SIGMOD '20). Association for Computing Machinery, New York, NY, USA, 543--557. https://doi.org/10.1145/3318464.3389693Google ScholarDigital Library
- Gary Shapiro, Christopher Natoli, and Vincent Gramoli. 2020. The Performance of Byzantine Fault Tolerant Blockchains. In 2020 IEEE 19th International Symposium on Network Computing and Applications (NCA). 1--8. https://doi.org/10.1109/NCA51143.2020.9306742Google ScholarCross Ref
- Ankur Sharma, Felix Martin Schuhknecht, Divya Agrawal, and Jens Dittrich. 2019. Blurring the Lines Between Blockchains and Database Systems: The Case of Hyperledger Fabric. In Proceedings of the 2019 International Conference on Management of Data (Amsterdam, Netherlands) (SIGMOD '19). ACM, New York, NY, USA, 105--122. https://doi.org/10.1145/3299869.3319883Google ScholarDigital Library
- Parth Thakkar, Senthil Nathan, and Balaji Viswanathan. 2018. Performance Benchmarking and Optimizing Hyperledger Fabric Blockchain Platform. In 2018 IEEE 26th International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS). 264--276. https://doi.org/10.1109/MASCOTS.2018.00034Google ScholarCross Ref
- An Binh Tran, Qinghua Lu, and Ingo Weber. 2018. Lorikeet: A Model-Driven Engineering Tool for Blockchain-Based Business Process Execution and Asset Management. In BPM (Dissertation/Demos/Industry) (CEUR Workshop Proceedings, Vol. 2196). CEUR-WS.org.Google Scholar
- Transaction Flow 2022. https://hyperledger-fabric.readthedocs.io/en/release-2.2/txflow.html. [Online; accessed 12-April-2023].Google Scholar
- Updating a channel configuration 2022. https://hyperledger-fabric.readthedocs.io/en/release-2.2/config_update.html. [Online; accessed 12-April-2023].Google Scholar
- Upgrading a smart contract 2022. https://hyperledger-fabric.readthedocs.io/en/release-2.2/deploy_chaincode.html#upgrading-a-smart-contract. [Online; accessed 12-April-2023].Google Scholar
- Gary Valentin, Michael Zuliani, Daniel C. Zilio, Guy Lohman, and Alan Skelley. 2000. DB2 advisor: an optimizer smart enough to recommend its own indexes. In Proceedings of 16th International Conference on Data Engineering. https://doi.org/10.1109/ICDE.2000.839397Google ScholarCross Ref
- Wil van der Aalst. 2009. Process-Aware Information Systems: Lessons to Be Learned from Process Mining. T. Petri Nets and Other Models of Concurrency 2 (01 2009), 1--26. https://doi.org/10.1007/978--3--642-00899--3_1Google ScholarCross Ref
- Wil Van Der Aalst. 2012. Process mining. Commun. ACM 55, 8 (2012), 76--83.Google ScholarDigital Library
- Wil van der Aalst, T. Weijters, and L. Maruster. 2004. Workflow mining: discovering process models from event logs. IEEE Transactions on Knowledge and Data Engineering 16, 9 (2004), 1128--1142. https://doi.org/10.1109/TKDE.2004.47Google ScholarDigital Library
- Boudewijn F van Dongen. 2017. https://doi.org/10.4121/12705737.v2Google ScholarCross Ref
- Boudewijn F Van Dongen, Ana Karla A de Medeiros, HMW Verbeek, AJMM Weijters, and Wil MP van Der Aalst. 2005. The ProM framework: A new era in process mining tool support. In International conference on application and theory of petri nets. Springer, 444--454.Google ScholarDigital Library
- A.J.M.M. Weijters, Wil M.P. Aalst, van der, and A.K. Alves De Medeiros. 2006. Process mining with the Heuristics Miner algorithm. Technische Universiteit Eindhoven.Google Scholar
- Karl Wüst and Arthur Gervais. 2018. Do you Need a Blockchain?. In 2018 Crypto Valley Conference on Blockchain Technology (CVCBT). 45--54. https://doi.org/10.1109/CVCBT.2018.00011Google ScholarCross Ref
- Xiaoqiong Xu, Gang Sun, Long Luo, Huilong Cao, Hongfang Yu, and Athanasios V. Vasilakos. 2021. Latency performance modeling and analysis for hyperledger fabric blockchain network. Information Processing & Management 58, 1 (2021), 102436. https://doi.org/10.1016/j.ipm.2020.102436Google ScholarCross Ref
- Qi Yang, Xiao Zeng, Yu Zhang, and Wei Hu. 2019. New Loan System Based on Smart Contract (BSCI '19). Association for Computing Machinery, New York, NY, USA, 121--126. https://doi.org/10.1145/3327960.3332395Google ScholarDigital Library
- Dirk A Zetzsche, Douglas W Arner, and Ross P Buckley. 2020. Decentralized Finance. Journal of Financial Regulation 6, 2 (09 2020), 172--203. https://doi.org/10.1093/jfr/fjaa010 arXiv:https://academic.oup.com/jfr/article-pdf/6/2/172/37064506/fjaa010.pdfGoogle ScholarCross Ref
Index Terms
- How To Optimize My Blockchain? A Multi-Level Recommendation Approach
Recommendations
Application of QPSO Algorithm in Aeroengine Maximum Thrust Optimization
CCIE '10: Proceedings of the 2010 International Conference on Computing, Control and Industrial Engineering - Volume 02A new and practical solution, Quantum-behaved Particle Swam Optimization (QPSO) algorithm, is applied to Aeroengine maximum thrust optimization implemented for some turbo fan engine. Simulation is carried out under different altitudes and velocities and ...
Blockchain Governance—A New Way of Organizing Collaborations?
The recent emergence of blockchains may be considered a critical turning point in organizing collaborations. We outline the historical background and the fundamental features of blockchains and present an analysis with a focus on their role as governance ...
Comments