Zenith seminar : 27/05/2019, 15h
Campus Saint Priest, BAT5-01.124

Bounce Blockchain: a secure, energy-efficient permissionless blockchain
Dennis Shasha
New York University & Inria International Chair in the Zenith team at LIRMM

Performing proof-of-work for the Bitcoin blockchain currently requires as much electricity as consumed by the country of Denmark. This enormous energy expenditure translates into higher costs for users (on the order of $US 6.00 or more per transaction) and is frankly ecologically irresponsible. As of this writing, it can be subverted (i.e. the blockchain can be forked) by a collusion attack of just a handful of data centers. This paper proposes the design of a cheap (less than $US 0.01 per transaction), essentially energy-free public blockchain called a Bounce Blockchain which cannot be forked in any reasonable failure scenario, even most Byzantine failure scenarios.
The basic idea is to send one or  more cubesats  into orbit, each equipped with a hardware security module. Users would send their transaction to the cubesats which would collect them into blocks, sign them, and send (bounce) them back to earth (and to one another).  Bounce Blockchain provides scalability through sharding (transactions will be partitioned over cubesats).
Because modern hardware security modules are tamper-resistant (become inoperable if tampered with) or tamper-responsive (erase their keys if tampered with), take their keys from physical processes, and have been validated, socio-technical protocols can ensure that it is infeasible to forge the identity of a hardware security module in a cubesat with another cubesat. If, however, some cubesats are destroyed, the blockchain will continue to execute correctly though some transactions will be lost. New cubesats can be sent up in short order as they are quite cheap to launch. If, in spite of these assurances, some cubesats fail traitorously, the blockchain can survive through algorithms similar to Practical Byzantine Fault Tolerance techniques.

Biography
Dennis Shasha is a Julius Silver Professor of computer science at the Courant Institute of New York University and an Associate Director of NYU Wireless. He works on meta-algorithms for machine learning to achieve guaranteed correctness rates, with biologists on pattern discovery for network inference; with physicists and financial people on algorithms for time series; on computational reproducibility; and on energy-efficient blockchains. Other areas of interest include database tuning as well as tree and graph matching. Because he likes to type, he has written six books of puzzles about a mathematical detective named Dr. Ecco, a biography about great computer scientists, and a book about the future of computing. He has also written five technical books about database tuning, biological pattern recognition, time series, DNA computing, resampling statistics, and causal inference in molecular networks. He has written the puzzle column for various publications including Scientific American, Dr. Dobb’s Journal, and currently the Communications of the ACM. He is a fellow of the ACM and an Inria International Chair in the Zenith team at LIRMM.

Seminar by Patrick Valduriez (Inria) at IBM Research, Rio de Janeiro, Brazil
Chair: Renan Francisco Santos Souza (IBM Brazil)
Time: Thu, Apr 25, 2019 11:00 AM – 12:30 PM
Location: Corcovado

The Case for Hybrid Transaction Analytical Processing
P. Valduriez
Inria and LIRMM, Montpellier, France

Abstract. Hybrid Transaction Analytical Processing (HTAP) is poised to revolutionize data management. By providing online analytics over operational data, HTAP systems open up new opportunities in many application domains where real-time decision is critical. Important use cases are proximity marketing, real-time pricing, risk monitoring, real-time fraud detection, etc. HTAP also simplifies data management, by removing the traditional separation between operational database and data warehouse/ data lake (no more ETLs!). However, a hard problem is scaling out transactions in mixed operational and analytical workloads over big data, possibly coming from different data stores (HDFS, SQL, NoSQL, …).
In this talk, I will introduce HTAP systems and illustrate with LeanXcale, a new generation HTAP DBMS that provides ultra-scalable transactions, big data analytics, SQL/JSON support and polystore capabilities.

Zenith seminar: 15/01/19, 15h – BAT5-02.124

Providing Online Data Analytical Support for Humans in the Loop of Computational Science and Engineering Applications

Renan Souza (IBM Research Brazil and UFRJ, Rio de Janeiro)

Abstract.Computational Scientists and Engineers analyze complex and big data during the execution of long-lasting data processing workflows in parallel machines. Depending on the results, they may need to steer the workflows by adapting predefined input data or settings. Being able to analyze the resulting data online knowing that certain results may have been directly influenced by specific actions they took is of paramount importance for result interpretability, reuse, and reproducibility. However, three major challenges hinder such analysis: online analytical support, user steering tracking, and efficient performance. In this talk, I will focus on online analytical support particularly for problems that require integrated data analysis by multi-workflows. Multi-workflows are distributed and parallel workflows that process data in heterogeneous data stores (e.g., DBMSs with various data models or raw data files) and share data dependencies. Such heterogeneity makes online analytical support even more challenging. We propose a solution to capture workflow provenance and domain data online to provide an integrated view over the data stores. We explore a real case study composed of four workflows that preprocess data for a Deep Learning classifier for Oil and Gas exploration. We show that our solution allows users to run online integrated data analysis of the multi-workflow data. Also, for certain scenarios, the performance of our solution is two orders of magnitude faster than a state-of-the-art solution.

Zenith seminar:  26/11/18, 14h – BAT5-02.249

Comparing Motif Discovery Techniques with Sequence Mining in the Context of Space-Time Series

Eduardo Ogasawara

CEFET / Rio de Janeiro)

Abstract:  A relevant area that is being explored in time series analysis community is finding patterns. Patterns are sub-sequences of time series that are related to some special properties or behaviors. A particular pattern that occurs a significant number of times in time series is denominated motif. Discovering motifs in time series data has been widely explored. Many time series techniques were developed to tackle this problem. However, various important time-series phenomena present different behaviors when observed at points of space (for example, series collected by sensors and IoT) and are better modeled as spatial-time series, in which each time series is associated to a position in space. When it comes to spatial-time series, it is possible to observe an open gap according to the literature review. Under such scenarios, motifs might not be discovered when we analyze each time series individually. They may be frequent if we consider different spatial-time series at some time interval. Finding patterns that are frequent in a constrained space and time, i.e., find spatial-time motifs, may enable us to comprehend how a phenomenon occurs concerning space and time. Meanwhile, database/data mining community studies the problem of discovering spatiotemporal sequential patterns, which appears in a broad range of applications. Many initiatives find sequences constrained by space and time, which can shed light to tackle spatial-time motif discovery. We are going to present these different techniques and potential challenges and solutions arising from these two communities in the context of spatial-time series motif discovery.

Short bio: Eduardo Ogasawara is a Professor of the Computer Science Department of the Federal Center for Technological Education of Rio de Janeiro (CEFET / RJ) since 2010. He holds a D.Sc. in Systems Engineering and Computer Science at COPPE / UFRJ. His background is in Databases, and his primary interest is Data Science. He is currently interested in data preprocessing, prediction, and pattern discovery regarding spatial-time series and also data-driven parallel and distributed processing. He is a member of the IEEE, ACM, INNS, and SBC. He led the creation of the Post-Graduate Program in Computer Science (PPCIC) of CEFET/RJ approved by CAPES in 2016. Currently, he is heading PPCIC.

Abstract : Thanks to smart disclosure initiatives and new regulations like GDPR, Personal Data Management Systems (PDMS) emerges. The PDMS paradigm empowers each individual with his complete digital environment. On the bright side, this opens the way to novel value-added services when crossing multiple sources of data of a given person or crossing the data of multiple people. Yet this paradigm shift towards user empowerment raises fundamental questions with regards to the appropriateness of the functionalities and the data management and protection techniques which are offered by existing solutions to laymen users. This presentation (1) compares PDMS alternatives in terms of functionalities and threat models, (2) derives a general set of functionality and security requirements that any PDMS should consider, (3) proposes a preliminary design building upon Trusted Execution Environments for an extensive and secure PDMS reference architecture, and (4) identifies a set of challenges of implementing such a PDMS.