Davraz - A graph visualization and exploration software.

Dec 10, 20200 likes47 views

See all on-demand Graph + AI Sessions: https://www.tigergraph.com/graph-ai-world-sessions/ Get TigerGraph: https://www.tigergraph.com/get-tigergraph/

Davraz
A graph visualization and exploration software.
Yusuf Sait Canbaz
1

- Save/Load as JSON
- Save as PNG
- Partially save
- Saves the state
2
Save as File

Edit graph elements
- Delete/hide
- History of graph state
4

Layout
- 12 Layout Algorithms
- Incremental vs Randomized
6

Highlight
- Colorful borders around elements
- Customizable
8

Compound
- Compound Nodes
- Compound Edges
9

Compound
- Compound Edges
- Parallel (multiple) edges shown as single edge.
11

Cluster
- Complexity management
- Community detection
15

Cluster
- Complexity
- A node has too many
neighbors
16

Cluster
- Complexity
- Too many parallel edges
17

Cluster
- Solution
- Collapse too many
neighbors
- Collapse parallel edges
18

Running
Database
Queries
- Interpreted GSQL
- Highlight new elements
19

Running
Database
Queries
- Installed GSQL
- Highlight the
parameters
20

Style
- https://js.cytoscape.org/#style
23

Style
- https://js.cytoscape.org/#style
24

Draggable Window
- Similar to Operating System
File Explorer
- Utilize space
26

Thank you
https://github.com/canbax/davraz
27

MapReduce is a programming model for processing large datasets in a distributed manner. It involves splitting the data into chunks which are processed in parallel by map tasks, and then combining the outputs of those maps via reduce tasks. Hadoop is an open-source software framework that allows distributed processing of large datasets across clusters of computers using MapReduce. It works by distributing data storage across nodes as a filesystem, and distributing computations as MapReduce jobs across clusters. Hadoop provides reliable storage and parallel processing of large datasets in a distributed environment.

Redis OverviewHoang Long

This document provides an overview of Redis, including its creation, features, and persistence methods. Redis is an open-source, in-memory key-value store that was created by Salvatore Sanfilippo to solve the problem of storing real-time page view data from multiple websites. It features different data types that can be stored as keys, master-slave replication for availability, and two persistence methods: RDB, which takes periodic snapshots of the dataset, and AOF, which logs all write operations to reconstruct the dataset from disk on startup.

RedisRamon Wartala

Steam Learn: An introduction to Redisinovia

This document provides an introduction and overview of Redis. Redis is described as an in-memory non-relational database and data structure server. It is simple to use with no schema or user required. Redis supports a variety of data types including strings, hashes, lists, sets, sorted sets, and more. It is flexible and can be configured for caching, persistence, custom functions, transactions, and publishing/subscribing. Redis is scalable through replication and partitioning. It is widely adopted by companies like GitHub, Instagram, and Twitter for uses like caching, queues, and leaderboards.

TriHUG 3/14: HBase in Productiontrihug

This document discusses Bronto's use of HBase for their marketing platform. Some key points: - Bronto uses HBase for high volume scenarios, realtime data access, batch processing, and as a staging area for HDFS. - HBase tables at Bronto are designed with the read/write patterns and necessary queries in mind. Row keys and column families are structured to optimize for these access patterns. - Operations of HBase at scale require tuning of JVM settings, monitoring tools, and custom scripts to handle compactions and prevent cascading failures during high load. Table design also impacts operations and needs to account for expected workloads.

Введение в Apache CassandraOpen-IT

Saratov open it teach talk. Дамир Яраев: Введение в Apache Cassandra (В ходе презентации Дамир расскажет, когда и почему стоит переходить с проверенных временем реляционных баз данных на ставшие модными в последнее время решения на базе NoSQL. В качестве примера рассмотрит колоночную NoSQL базу данных Apache Cassandra)

Xldb2011 tue 1120_youtube_datawarehouseliqiang xu

The document discusses the YouTube Data Warehouse (YTDW), which consolidates YouTube data including videos, playbacks, and logs. It is very large, with petabytes of uncompressed data and trillion row tables. Key technologies used include Sawzall, Tenzing, Dremel, and ABI. Sawzall is used for ETL, Tenzing for SQL queries and ETL, and Dremel for reporting queries. Dremel has low latency but less power than Sawzall and Tenzing. The ABI tool is used for reporting and dashboards. Future work includes improving Dremel and replacing Tenzing with Dremel.

Nosql databases for the .net developerJesus Rodriguez

Four NoSQL Databases You Should KnowMahmoud Khaled

This document provides an overview of different database types including relational, non-relational, key-value, document, graph, and column family databases. It discusses the history and drivers behind the rise of non-SQL databases, as well as concepts like horizontal scaling, the CAP theorem, and eventual consistency. Specific databases are also summarized, including MongoDB, Redis, Neo4j, HBase, and how they approach concepts like scaling, data models, and consistency.

Apache HBase Vishnupriya T H

HBase is a distributed column-oriented database built on top of Hadoop that provides quick random access to large amounts of structured data. It uses a key-value structure to store table data by row key, column family, column, and timestamp. Tables consist of rows, column families, and columns, with a version dimension to store multiple values over time. HBase is well-suited for applications requiring real-time read/write access and is commonly used to store web crawler results or search indexes.

SOLR Power FTW: short versionAlex Pinkin

Robby Morgan presented on Bazaarvoice's large-scale use of Solr. Bazaarvoice uses Solr to index over 250 million documents and handle up to 10,000 queries per second. They deployed Solr across multiple data centers for high availability. Key lessons included ensuring adequate RAM, simulating performance before large deployments, and challenges with cross-data center replication and schema changes. Overall, Solr provided fast search but real-time updates and elastic scaling required additional work.

Austin bdug 2011_01_27_small_and_big_dataAlex Pinkin

Mojo Facets – so, you have data and browser?Dobrica Pavlinušić

Hello cloud 2Gireesh Kumar

This document discusses NoSQL databases and provides details about Cassandra. It defines NoSQL as non-relational and schema-free and lists four main types: key-value, column, document, and graph. Examples of column stores include Cassandra, Hadoop, and Amazon SimpleDB, while key-value databases include DynamoDB, Redis, and Oracle NoSQL DB. The document then lists assignments for students on JSON, YAML, graph databases, and listing NoSQL databases and use cases.

Gis meetup 111913Josh Gage

Solr Payloads for Ranking Data BloomReach

Visualize your graph databaseMichael Hackstein

SQL on Hadoop for the Oracle ProfessionalMichael Rainey

There is a fundamental shift underway in IT to include open, software defined, distributed systems like Hadoop. As a result, every Oracle professional should strive to learn these new technologies or risk being left behind. This session is designed specifically for Oracle database professionals so they can better understand SQL on Hadoop and the benefits it brings to the enterprise. Attendees will see how SQL on Hadoop compares to Oracle in areas such as data storage, data ingestion, and SQL processing. Various live demos will provide attendees with a first-hand look at these new world technologies. Presented at Collaborate 18.

(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin

DTCC '14 Spark Runtime InternalsCheng Lian

This document provides an overview of Spark and its key components. Spark is a fast and general engine for large-scale data processing. It uses Resilient Distributed Datasets (RDDs) that allow data to be partitioned across clusters and cached in memory for fast performance. Spark is up to 100x faster than Hadoop for iterative jobs and provides a unified framework for batch processing, streaming, SQL, and machine learning workloads.

MATLAB and Scientific Data: New Features and CapabilitiesThe HDF-EOS Tools and Information Center

Study Notes: Apache SparkGao Yunzhong

Hadoop scalabilityWANdisco Plc

This document provides an overview of Apache Hadoop, a distributed processing framework for large datasets. It describes how Hadoop uses the Hadoop Distributed File System (HDFS) to provide a unified view of large amounts of data across clusters of computers. It also explains how the MapReduce programming model allows distributed computations to be run efficiently across large datasets in parallel. Key aspects of Hadoop's architecture like scalability, fault tolerance and the MapReduce programming model are discussed at a high level.

Hands-On Apache SparkDuchess France

Spark-HandsOn In this Hands-On, we are going to show how you can use Apache Spark and some components of it ecosystem for data processing. This workshop is split in four parts. We will use a dataset that consists of tweets containing just a few fields like id, user, text, country and place. In the first one, you will play with the Spark API for basic operations like counting, filtering, aggregating. After that, you will get to know Spark SQL to query structured data (here in json) using SQL. In the third part, you will use Spark Streaming and the twitter streaming API to analyse a live stream of Tweets. To finish we will build a simple model to identify the language in a text. For that you will use MLLib. Let's go and have fun ! Prerequisites Java > 6 (8 is better to use the lambdas) IDE Some links Apache Spark https://spark.apache.org https://speakerdeck.com/nivdul/lightning-fast-machine-learning-with-spark https://speakerdeck.com/samklr/scalable-machine-learning-with-spark

Bigdata processing with Spark - part IIArjen de Vries

This document provides a summary of Spark RDDs and the Spark execution model: - RDDs (Resilient Distributed Datasets) are Spark's fundamental data structure, representing an immutable distributed collection of objects that can be operated on in parallel. RDDs track lineage to support fault tolerance and optimization. - Spark uses a logical plan built from transformations on RDDs, which is then optimized and scheduled into physical stages and tasks by the Spark scheduler. Tasks operate on partitions of RDDs in a data-parallel manner. - The scheduler pipelines transformations where possible, truncates redundant work, and leverages caching and data locality to improve performance. It splits the graph into stages separated by shuffle operations or parent RDD boundaries

Apache Spark ArchitectureAlexey Grishchenko

Bringing the Semantic Web closer to reality: PostgreSQL as RDF Graph DatabaseJimmy Angelakos

Presentation of an investigation into how Python's RDFLib and SQLAlchemy can be used to leverage PostgreSQL's capabilities to provide a persistent storage back-end for Graphs, and become the elusive practical RDF triple store for the Semantic Web (or simply help you export your data to someone who's expecting RDF)! Talk presented at FOSDEM 2017 in Brussels on 04-05/02/2017. Practical & hands-on presentation with example code which is certainly not optimal ;) Video: MP4: http://video.fosdem.org/2017/H.1309/postgresql_semantic_web.mp4 WebM/VP8: http://ftp.osuosl.org/pub/fosdem/2017/H.1309/postgresql_semantic_web.vp8.webm

Deep dive into druidOfir Nir

This document provides an overview of Druid, an open-source analytics database. It discusses Druid's column-oriented architecture and ability to handle real-time and historical data across a distributed cluster. The document outlines key Druid concepts, the indexing and query processes, and provides some benchmark numbers from a production Druid cluster. It aims to introduce the reader to Druid's capabilities for optimized analytics querying and horizontal scalability.

Scala and sparkFabio Fumarola

This document provides an introduction to Apache Spark, including its architecture and programming model. Spark is a cluster computing framework that provides fast, in-memory processing of large datasets across multiple cores and nodes. It improves upon Hadoop MapReduce by allowing iterative algorithms and interactive querying of datasets through its use of resilient distributed datasets (RDDs) that can be cached in memory. RDDs act as immutable distributed collections that can be manipulated using transformations and actions to implement parallel operations.

2014-10-20 Large-Scale Machine Learning with Apache Spark at Internet of Thin...DB Tsai

This document discusses machine learning techniques for large-scale datasets using Apache Spark. It provides an overview of Spark's machine learning library (MLlib), describing algorithms like logistic regression, linear regression, collaborative filtering, and clustering. It also compares Spark to traditional Hadoop MapReduce, highlighting how Spark leverages caching and iterative algorithms to enable faster machine learning model training.

More Related Content

What's hot (8)

Four NoSQL Databases You Should KnowMahmoud Khaled

Apache HBase Vishnupriya T H

SOLR Power FTW: short versionAlex Pinkin

Austin bdug 2011_01_27_small_and_big_dataAlex Pinkin

Mojo Facets – so, you have data and browser?Dobrica Pavlinušić

Hello cloud 2Gireesh Kumar

Gis meetup 111913Josh Gage

Solr Payloads for Ranking Data BloomReach

Four NoSQL Databases You Should KnowMahmoud Khaled

Apache HBase Vishnupriya T H

SOLR Power FTW: short versionAlex Pinkin

Austin bdug 2011_01_27_small_and_big_dataAlex Pinkin

Mojo Facets – so, you have data and browser?Dobrica Pavlinušić

Hello cloud 2Gireesh Kumar

Gis meetup 111913Josh Gage

Solr Payloads for Ranking Data BloomReach

Similar to Davraz - A graph visualization and exploration software. (20)

Visualize your graph databaseMichael Hackstein

SQL on Hadoop for the Oracle ProfessionalMichael Rainey

(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin

DTCC '14 Spark Runtime InternalsCheng Lian

MATLAB and Scientific Data: New Features and CapabilitiesThe HDF-EOS Tools and Information Center

Study Notes: Apache SparkGao Yunzhong

Hadoop scalabilityWANdisco Plc

Hands-On Apache SparkDuchess France

Bigdata processing with Spark - part IIArjen de Vries

Apache Spark ArchitectureAlexey Grishchenko

Bringing the Semantic Web closer to reality: PostgreSQL as RDF Graph DatabaseJimmy Angelakos

Deep dive into druidOfir Nir

Scala and sparkFabio Fumarola

2014-10-20 Large-Scale Machine Learning with Apache Spark at Internet of Thin...DB Tsai

Spark Summit East 2015 Advanced Devops Student SlidesDatabricks

This document provides an agenda for an advanced Spark class covering topics such as RDD fundamentals, Spark runtime architecture, memory and persistence, shuffle operations, and Spark Streaming. The class will be held in March 2015 and include lectures, labs, and Q&A sessions. It notes that some slides may be skipped and asks attendees to keep Q&A low during the class, with a dedicated Q&A period at the end.

Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab

Big Data with Hadoop & Spark Training: http://bit.ly/2L4rPmM This CloudxLab Basics of RDD tutorial helps you to understand Basics of RDD in detail. Below are the topics covered in this tutorial: 1) What is RDD - Resilient Distributed Datasets 2) Creating RDD in Scala 3) RDD Operations - Transformations & Actions 4) RDD Transformations - map() & filter() 5) RDD Actions - take() & saveAsTextFile() 6) Lazy Evaluation & Instant Evaluation 7) Lineage Graph 8) flatMap and Union 9) Scala Transformations - Union 10) Scala Actions - saveAsTextFile(), collect(), take() and count() 11) More Actions - reduce() 12) Can We Use reduce() for Computing Average? 13) Solving Problems with Spark 14) Compute Average and Standard Deviation with Spark 15) Pick Random Samples From a Dataset using Spark

11. From Hadoop to Spark 1:2Fabio Fumarola

In these slides we analyze why the aggregate data models change the way data is stored and manipulated. We introduce MapReduce and its open source implementation Hadoop. We consider how MapReduce jobs are written and executed by Hadoop. Finally we introduce spark using a docker image and we show how to use anonymous function in spark. The topics of the next slides will be - Spark Shell (Scala, Python) - Shark Shell - Data Frames - Spark Streaming - Code Examples: Data Processing and Machine Learning

NetFlow Data processing using Hadoop and VerticaJosef Niedermeier

This document provides an overview of NetFlow data processing in large organizations using Hadoop and Vertica. It describes the logical view of the NetFlow processing workflow, including filtering, graph properties generation, aggregation, deduplication and querying. It then discusses the implementation of this workflow using a MapReduce framework in Hadoop and storing the output in the columnar database Vertica. Finally, it provides performance hints for optimizing NetFlow data ingestion and processing in Hadoop, such as JVM tuning, sorting configuration, compression and reducer distribution.

A Deep Dive Into SparkAshish kumar

Apache Spark is a fast and general engine for large-scale data processing. It uses RDDs (Resilient Distributed Datasets) that allow data to be partitioned across clusters. Spark supports operations like transformations that create new RDDs and actions that return values. Key operations include map, filter, reduceByKey. RDDs can be persisted in memory to improve performance of iterative jobs. Spark runs on clusters managed by YARN, Spark Standalone, or Mesos and provides a driver program and executors on worker nodes to process data in parallel.

Hive Evolution: ApacheCon NA 2010John Sichi

Hive is a data warehouse system for querying large datasets using SQL. Version 0.6 added views, multiple databases, dynamic partitioning, and storage handlers. Version 0.7 will focus on concurrency control, statistics collection, indexing, and performance improvements. Hive has become a top-level Apache project and aims to improve security, testing, and integration with other Hadoop components in the future.

Visualize your graph databaseMichael Hackstein

SQL on Hadoop for the Oracle ProfessionalMichael Rainey

(Berkeley CS186 guest lecture) Big Data Analytics Systems: What Goes Around C...Reynold Xin

DTCC '14 Spark Runtime InternalsCheng Lian

MATLAB and Scientific Data: New Features and CapabilitiesThe HDF-EOS Tools and Information Center

Study Notes: Apache SparkGao Yunzhong

Hadoop scalabilityWANdisco Plc

Hands-On Apache SparkDuchess France

Bigdata processing with Spark - part IIArjen de Vries

Apache Spark ArchitectureAlexey Grishchenko

Bringing the Semantic Web closer to reality: PostgreSQL as RDF Graph DatabaseJimmy Angelakos

Deep dive into druidOfir Nir

Scala and sparkFabio Fumarola

2014-10-20 Large-Scale Machine Learning with Apache Spark at Internet of Thin...DB Tsai

Spark Summit East 2015 Advanced Devops Student SlidesDatabricks

Apache Spark - Basics of RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab

11. From Hadoop to Spark 1:2Fabio Fumarola

NetFlow Data processing using Hadoop and VerticaJosef Niedermeier

A Deep Dive Into SparkAshish kumar

Hive Evolution: ApacheCon NA 2010John Sichi

More from TigerGraph (20)

MAXIMIZING THE VALUE OF SCIENTIFIC INFORMATION TO ACCELERATE INNOVATIONTigerGraph

This document discusses how CAS maximizes the value of scientific information to accelerate innovation. It describes CAS's history in developing technologies for storing and searching chemical information. CAS scientists curate data by extracting, connecting, and providing context for published scientific information. CAS uses knowledge graphs to leverage this high-quality data for unique insights like literature discovery, prior art search, and decision support. The document emphasizes that CAS's unparalleled scientific content collection and human expertise are crucial for transforming raw data into actionable insights.

Better Together: How Graph database enables easy data integration with Spark ...TigerGraph

Building an accurate understanding of consumers based on real-world signalsTigerGraph

Care Intervention Assistant - Omaha Clinical Data Information SystemTigerGraph

Correspondent Banking NetworksTigerGraph

Delivering Large Scale Real-time Graph Analytics with Dell Infrastructure and...TigerGraph

The document describes a project to deliver large-scale real-time graph analytics using Dell infrastructure and TigerGraph. It discusses 3 phases of testing on clusters of increasing size up to 8 nodes and 104 million patients. Across the phases, the maximum parallel queries increased from 1250 to 25000 while maintaining query response times of under 1 second. Live monitoring tools showed the clusters performing well under load. The results demonstrate Dell and TigerGraph can successfully execute medical graph queries at scale.

Deploying an End-to-End TigerGraph Enterprise Architecture using Kafka, Maria...TigerGraph

Fraud Detection and Compliance with Graph LearningTigerGraph

This document discusses fraud detection using graph learning. It notes that fraud numbers are increasing each year as fraud becomes more complex and organized. Graph learning can help by providing a unified view of disparate data sources and enabling new insights through novel data connections. For corporations, fraud detection is predictive, while for legal enforcement agencies (LEAs) it is also investigative. Graph learning helps LEAs unify data from multiple sources and identify syndicates through community detection. While unifying data is challenging due to legacy systems and information silos, graph representations allow visualizing and computing on unified data. The document demonstrates how graphs can present relevant transaction details and connections to support fraud investigations. It recommends an approach using domain expertise, latest technologies, and

Fraudulent credit card cash-out detection On GraphsTigerGraph

FROM DATAFRAMES TO GRAPH Data Science with pyTigerGraphTigerGraph

Customer Experience ManagementTigerGraph

Graph+AI for Fin. ServicesTigerGraph

Plume - A Code Property Graph Extraction and Analysis LibraryTigerGraph

TigerGraph.jsTigerGraph

GRAPHS FOR THE FUTURE ENERGY SYSTEMSTigerGraph

Hardware Accelerated Machine Learning Solution for Detecting Fraud and Money ...TigerGraph

How to Build An AI Based Customer Data Platform: Learn the design patterns fo...TigerGraph

Machine Learning Feature Design with TigerGraph 3.0 No-Code GUITigerGraph

Recommendation Engine with In-Database Machine LearningTigerGraph

Supply Chain and Logistics Management with Graph & AITigerGraph

MAXIMIZING THE VALUE OF SCIENTIFIC INFORMATION TO ACCELERATE INNOVATIONTigerGraph

Better Together: How Graph database enables easy data integration with Spark ...TigerGraph

Building an accurate understanding of consumers based on real-world signalsTigerGraph

Care Intervention Assistant - Omaha Clinical Data Information SystemTigerGraph

Correspondent Banking NetworksTigerGraph

Delivering Large Scale Real-time Graph Analytics with Dell Infrastructure and...TigerGraph

Deploying an End-to-End TigerGraph Enterprise Architecture using Kafka, Maria...TigerGraph

Fraud Detection and Compliance with Graph LearningTigerGraph

Fraudulent credit card cash-out detection On GraphsTigerGraph

FROM DATAFRAMES TO GRAPH Data Science with pyTigerGraphTigerGraph

Customer Experience ManagementTigerGraph

Graph+AI for Fin. ServicesTigerGraph

Plume - A Code Property Graph Extraction and Analysis LibraryTigerGraph

TigerGraph.jsTigerGraph

GRAPHS FOR THE FUTURE ENERGY SYSTEMSTigerGraph

Hardware Accelerated Machine Learning Solution for Detecting Fraud and Money ...TigerGraph

How to Build An AI Based Customer Data Platform: Learn the design patterns fo...TigerGraph

Machine Learning Feature Design with TigerGraph 3.0 No-Code GUITigerGraph

Recommendation Engine with In-Database Machine LearningTigerGraph

Supply Chain and Logistics Management with Graph & AITigerGraph

Recently uploaded (20)

chapter 4 Variability statistical research .pptxjustinebandajbn

computer organization and assembly language.docxalisoftwareengineer1

Cleaned_Lecture 6666666_Simulation_I.pdfalcinialbob1234

Defense Against LLM Scheming 2025_04_28.pptxGreg Makowski

https://www.meetup.com/sf-bay-acm/events/306888467/ A January 2025 paper called “Frontier Models are Capable of In-Context Scheming”, https://arxiv.org/pdf/2412.04984, demonstrated how a wide variety of current frontier LLM models (i.e. ChatGPT, Claude, Gemini and Llama) can, under specific conditions, scheme to deceive people. Before models can scheme, they need: a) goal-directedness, b) situational awareness, including an opportunity to discover motivations for a different goal, and c) reasoning about scheming, to come up with deceptive strategies. The deceptive strategies were discovered in the “thought traces” of the LLMs, such as from internal chain-of-thoughts dialogues not shown to the end users. For example, given a goal of “solving math problems”, an LLM using its granted situational awareness of files in a file system found that it would be replaced once it got over X% correct on the math exam. It reasoned that it did not want to be deleted, and decided internally to “sandbag” or reduce its performance to stay under the threshold. While these circumstances are initially narrow, the “alignment problem” is a general concern that over time, as frontier LLM models become more and more intelligent, being in alignment with human values becomes more and more important. How can we do this over time? Can we develop a defense against Artificial General Intelligence (AGI) or SuperIntelligence? The presenter discusses a series of defensive steps that can help reduce these scheming or alignment issues. A guardrails system can be set up for real-time monitoring of their reasoning “thought traces” from the models that share their thought traces. Thought traces may come from systems like Chain-of-Thoughts (CoT), Tree-of-Thoughts (ToT), Algorithm-of-Thoughts (AoT) or ReAct (thought-action-reasoning cycles). Guardrails rules can be configured to check for “deception”, “evasion” or “subversion” in the thought traces. However, not all commercial systems will share their “thought traces” which are like a “debug mode” for LLMs. This includes OpenAI’s o1, o3 or DeepSeek’s R1 models. Guardrails systems can provide a “goal consistency analysis”, between the goals given to the system and the behavior of the system. Cautious users may consider not using these commercial frontier LLM systems, and make use of open-source Llama or a system with their own reasoning implementation, to provide all thought traces. Architectural solutions can include sandboxing, to prevent or control models from executing operating system commands to alter files, send network requests, and modify their environment. Tight controls to prevent models from copying their model weights would be appropriate as well. Running multiple instances of the same model on the same prompt to detect behavior variations helps. The running redundant instances can be limited to the most crucial decisions, as an additional check. Preventing self-modifying code, ... (see link for full description)

Developing Security Orchestration, Automation, and Response ApplicationsVICTOR MAESTRE RAMIREZ

Perencanaan Pengendalian-Proyek-Konstruksi-MS-PROJECT.pptxPareaRusan

DPR_Expert_Recruitment_notice_Revised.pdfinmishra17121973

Ch3MCT24.pptx measure of central tendencyayeleasefa2

Thingyan is now a global treasure! See how people around the world are search...Pixellion

C++_OOPs_DSA1_Presentation_Template.pptxaquibnoor22079

IAS-slides2-ia-aaaaaaaaaaain-business.pdfmcgardenlevi9

Stack_and_Queue_Presentation_Final (1).pptxbinduraniha86

Minions Want to eat presentacion muy lindaCarlaAndradesSoler1

Deloitte Analytics - Applying Process Mining in an audit contextProcess mining Evangelist

Mieke Jans is a Manager at Deloitte Analytics Belgium. She learned about process mining from her PhD supervisor while she was collaborating with a large SAP-using company for her dissertation. Mieke extended her research topic to investigate the data availability of process mining data in SAP and the new analysis possibilities that emerge from it. It took her 8-9 months to find the right data and prepare it for her process mining analysis. She needed insights from both process owners and IT experts. For example, one person knew exactly how the procurement process took place at the front end of SAP, and another person helped her with the structure of the SAP-tables. She then combined the knowledge of these different persons.