About

Anand Tripathi is a professor in the Department of Computer Science & Engineering at the University of Minnesota. He joined the department in 1984 as an assistant professor and was promoted to a full professor in 2001. His educational background includes a B. Tech in Electrical Engineering from the Indian Institute of Technology Bombay, and both an M.S. and a Ph.D. in Electrical Engineering from the University of Texas at Austin. Prior to his academic career, he served as a scientist at the Bhabha Atomic Research Center in India and held positions at Honeywell Inc. and the National Science Foundation. His research focuses on distributed systems, with specific emphasis on novel programming paradigms, system security, fault-tolerance, and scalable architectures. His current research is on Blockchain systems. Some of his recent projects include developing a framework for parallel programming of large-scale graph problems on cluster computers, techniques for scalable transaction management in key-value based data storage systems, and a generative programming framework for secure collaboration systems. He teaches courses on operating systems and distributed systems. Anand Tripathi has been recognized as an IEEE Fellow since 2007 and has served as an IEEE Distinguished Visitor since 2019.

Research topics

• Geology
• Seismology
• Oceanography

Selected publications

• CodeTogether: Real-time Code Editor Application for Collaborative Programming

  International Journal for Research in Applied Science and Engineering Technology · 2025-05-21

  articleOpen accessSenior author

  Abstract: The world of Internet is growing rapidly, many applications that previously created on the desktop start moving to the web. Many applicationscouldbeaccessedanytimeandanywhereeasilyusingInternet.Developersneedtoolstocreatetheirapplications, oneofthem namedcodeeditor.Thepurposeofthisresearchistodesignanddevelopareal-timecodeeditorapplicationusing websocket technologytohelpuserscollaboratewhileworkingontheproject.Thisapplicationprovidesafeaturewhereuserscancollaborateonaprojec tinreal-time.Theauthorsusinganalysismethodologywhichconductingonastudyofthecurrent code editor applications, distributing questionnaires and conducting on literature study.CodeTogether is a web application that provides workspace to writing, perform, display the results of the code through the terminal, and collaborate with other users in real-time. The application main features are providing workspace to make, execute and build the source code, real-time collaboration, chat, and build the terminal. This application supports C, C++, and Java programming languages

  PublisherDOI

• Did the Flores backarc thrust rupture offshore during the 2018 Lombok earthquake sequence in Indonesia?

  Geophysical Journal International · 2020 · 57 citations

  Senior authorCorresponding
  • Geology
  • Seismology
  • Oceanography

  SUMMARY The Flores thrust forms the west segment (∼450 km) of a very active, ∼E–W striking, ∼800-km-long backarc thrust along the east Sunda Arc. In 2018, a deadly earthquake sequence composed of ~110 M4+ events rattled the Indonesian island of Lombok near the Flores thrust and caused tremendous damage on the island, however what is the nature of rupturing during this earthquake sequence remains unknown. Here, using a total of 2120 km of high-resolution seismic profiles covering ∼300 km of the Flores thrust off Bali, Lombok and Sumbawa, in addition to earthquake data and InSAR measurements, we investigated the active thrusting during this earthquake sequence. Our seismic interpretation and structural mapping show that offshore north of Lombok and Bali, the remarkable Flores thrust is essentially blind, deforming the seabed by folds, not faults. The Lombok earthquakes were all shallow thrust events with depth <40 km and occurred within ∼35 km north of the Rinjani volcano beneath the Lombok Island and its northern extremity. The InSAR measurements suggested that the most of the crustal deformation caused by these earthquakes occurred the north the and northeast of the island. The maximum vertical deformation was ∼36 cm near the northwest margin of the island, caused by the 5th August Mw 6.9 event. These observations combined with the presence of blind thrusts off Lombok suggest that the offshore portion of Flores thrust did not rupture during the 2018 Lombok earthquake sequence; the most coseismic slip must have occurred along a deep-rooted, north-verging basal fault and a range of imbricate thrusts beneath the north of the island, not along the buried thrusts offshore. Despite being blind off Lombok and Bali, the Flores thrust can still pose tsunami threats to the adjacent population centres by rupturing the seafloor during future large earthquakes (M > 7) that occur directly on the offshore blind thrusts, not beneath the island like the Lombok sequence. The proximity of the Rinjani volcano and thrust earthquakes suggests a possible role of volcanic activity (e.g. magmatic fluids and gas migration, stress change induced by pressurized magma chamber) in inducing the Lombok earthquakes.

  PublisherDOI

• Incremental parallel computing for continuous queries in dynamic graphs using a transactional model

  Concurrency and Computation Practice and Experience · 2018-05-04 · 1 citations

  article1st authorCorresponding

  Summary In dynamically evolving graphs, one may be interested in continuously observing certain properties of the graph. One approach for continuous monitoring is to re‐execute the graph analytics program on the entire graph after it is updated. However, this approach can lead to high computation cost and latency in case of large graphs. An alternate approach, which we present in this paper, is to execute the analytics program only initially and then perform incremental computations for supporting continuous queries as the graph is modified. The goal of our work is to develop incremental parallel computing techniques to continuously monitor a graph as it is updated to check for the properties of interest. We present the results of our investigation of utilizing a transactional model of parallel programming for supporting continuous queries on dynamic and evolving graphs. In our model, the graph updates are performed as transactions, which trigger the execution of a set of transactional tasks to perform incremental computations. In our testbed system, the graph data is stored in the RAM of cluster nodes, and continuous queries involve the parallel execution of transactional tasks on the cluster nodes. Using a set of graph problems, we illustrate this approach and evaluate its performance.

  PublisherDOI

• A Transactional Model for Parallel Programming of Graph Applications on Computing Clusters

  2017-06-01 · 3 citations

  articleOpen access1st authorCorresponding

  We present here the results of our investigation of a transactional model of parallel programming on cluster computing systems. This model is specifically targeted for graph applications with the goal of harnessing unstructured parallelism inherently present in many such problems. In this model, tasks for vertex-centric computations are executed optimistically in parallel as serializable transactions. A key-value based globally shared object store is implemented in the main memory of the cluster nodes for storing the graph data. Task computations read and modify data in the distributed global store, without any explicitly programmed message-passing in the application code. Based on this model we developed a framework for parallel programming of graph applications on computing clusters. We present here the programming abstractions provided by this framework and its architecture. Using several graph problems we illustrate the simplicity of the abstractions provided by this model. These problems include graph coloring, k-nearest neighbors, and single-source shortest path computation. We also illustrate how incremental computations can be supported by this programming model. Using these problems we evaluate the transactional programming model and the mechanisms provided by this framework.

  PublisherOA PDFDOI

• Investigation of a Transactional Model for Incremental Parallel Computing in Dynamic Graphs

  University of Minnesota Digital Conservancy (University of Minnesota) · 2017-05-25

  articleOpen access1st authorCorresponding

  In many applications involving dynamic graph structures one may be interested in continuously observing certain properties of interest. We present here the result of our investigation of utilizing a transactional model of parallel programming for supporting continuous queries on dynamic and evolving graph structures. The goal of our work is to continuously monitor a graph data structure as it is updated to check for the properties of interest. One approach for continuous monitoring is to re-execute the graph analytics program on the entire graph structure after it is updated. However, this approach can lead to high computation cost and latency in case of large graphs. An alternate approach is to execute the analytics program only initially, and then perform incremental computations for supporting continuous queries as the graph data is modified. In our model, the graph updates are performed as transactions, which trigger execution of a set of transactional tasks to perform computations for a continuous query. In our testbed system, the graph data is stored in the RAM of cluster nodes, and continuous queries involve parallel execution of transactional tasks on cluster nodes. Using a set of graph problems we illustrate this approach and its performance benefits for supporting continuous queries in dynamic graph data structure.

  Publisher

• Design of a Location-Based Publish/Subscribe Service Using a Graph-Based Computing Model

  2017-10-01

  articleOpen access1st authorCorresponding

  We present here the initial results of our investigation of a system architecture for location-based publish/subscribe services utilizing a graph-based model for managing data and computations. This architecture is implemented on a cluster computer using the facilities and the computation model provided by the Beehive framework which supports a transactional model of parallel computing on dynamic graph data structures. We implemented a Museum Visitor Service as an example of a location-based publish/subscribe system to study and evaluate the performance this approach. This service includes features utilizing location-based publish/subscribe functions for supporting coordination and collaboration among members in a social group visiting the museum. We implemented a testbed system for this service and evaluated its performance on a cluster computer. Our work also illustrates that weaker consistency models for transactions can be utilized in such services to achieve higher performance and scalability.

  PublisherOA PDFDOI

• Attentiveness to eyes predicts generosity in a reputation-relevant context

  Evolution and Human Behavior · 2017-08-03 · 18 citations

  article
  PublisherDOI

• A Transaction Model with Multilevel Consistency for Shared Data in Distributed Groupware Systems

  2016-11-01 · 4 citations

  articleOpen access1st authorCorresponding

  In groupware systems a broad range of requirements for user coordination and data consistency need to be supported. The notions of event causality and user awareness are central in such requirements. Traditional transaction models supported in general purpose database management systems with strong consistency guarantees have been found to be unsuitable for groupware systems. Weaker models for data consistency are needed for user awareness and cooperative activities. Objects in the shared workspace need to be managed with different consistency guarantees. Towards such requirements, we examine here the applicability of a distributed transaction management model which supports multilevel consistency. The consistency levels supported in this model include serializable transactions for strong consistency and weaker consistency models such as Causal Snapshot Isolation (CSI), CSI with commutative updates, and CSI with asynchronous updates. We review the coordination and data consistency requirements in groupware systems. We show using two examples how replicated shared data in distributed groupware systems can be managed with multiple consistency levels using this model.

  PublisherOA PDFDOI

• Design and Evaluation of a Transaction Model with Multiple Consistency Levels for Replicated Data

  University of Minnesota Digital Conservancy (University of Minnesota) · 2015-05-20 · 2 citations

  articleOpen access1st authorCorresponding

  We present here a transaction model which simultaneously supports different consistency levels, which include serializable transactions for strong consistency, and weaker consistency models such as causal snapshot isolation (CSI), CSI with commutative updates, and CSI with asynchronous concurrent updates. This model can be useful in managing replicated data with different consistency guarantees to make suitable tradeoffs between data availability, performance, and consistency of different data items. Data and the associated transactions are organized in a hierarchy which is based on consistency levels. Certain rules are imposed on transactions to constrain information flow across data at different levels in this hierarchy to ensure the required consistency guarantees. The building block for this transaction model is the snapshot isolation model. We present an example of an e-commerce application structured with data items and transactions defined at different consistency levels. We have implemented a testbed system for replicated data management based on the proposed multilevel consistency model. We present here the results of our experiments with this ecommerce application to demonstrate the benefits of this model.

  Publisher

• A transaction model for management of replicated data with multiple consistency levels

  2015-10-01 · 6 citations

  articleOpen access1st authorCorresponding

  We present a transaction model which simultaneously supports different consistency levels, which include serializable transactions for strong consistency, and weaker consistency models such as causal snapshot isolation (CSI), CSI with commutative updates, and CSI with asynchronous updates. This model is useful in managing large-scale replicated data with different consistency guarantees to make suitable trade-offs between data consistency and performance. Data and the associated transactions are organized in a hierarchy which is based on consistency levels. Certain rules are imposed on transactions to constrain information flow across data at different levels in this hierarchy to ensure the required consistency guarantees. The building block for this transaction model is the snapshot isolation model. We present an example of an e-commerce application structured with data items and transactions defined at different consistency levels. We have implemented a testbed system for replicated data management based on the proposed multilevel consistency model. We present here the results of our experiments with this e-commerce application to demonstrate the benefits of this model.

  PublisherOA PDFDOI

Recent grants

• Secure Context-Aware Distributed Collaboration Systems

  NSF · $400k · 2004–2008

• CSR:Small: Scalable Transaction Management and Consistency Models for Cloud Data Storage Systems

  NSF · $461k · 2013–2018

• CRI:IAD: Infrastructure for Research in Spatio-Temporal and Context-Aware Systems and Applications

  NSF · $140k · 2007–2011

Frequent coauthors

• Tanvir Ahmed

  22 shared

• Devdatta Kulkarni

  The University of Texas at Austin

  15 shared

• Vinit Padhye

  University of Minnesota

  11 shared

• Khaled Day

  Sultan Qaboos University

  9 shared

• Peter J.J. Kamp

  University of Waikato

  9 shared

• Campbell S. Nelson

  University of Waikato

  8 shared

• N.M. Karnik

  King Edward Memorial Hospital and Seth G.S. Medical College

  8 shared

• John Eberhard

  University of Minnesota

  6 shared

Labs

• Anand TripathiPI

Awards & honors

• IEEE Fellow (2007)
• IEEE Distinguished Visitor since 2019