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This paper proposes a real-time approach to detect the 2D pose of multiple people in an image. OpenPose is an open-source multi-person detection system supporting the body, hand, foot, and facial key points. Human pose estimation is focused on finding individuals and their pose from an image. This problem is quite challenging and is quite complex to determine the number of people that can appear in an image. Moreover, the interferences caused due to contact or proximity between people make the association of parts quite challenging. The current state-of-the-art techniques use a top-down strategy that estimates the pose of each person in each of the detected regions. This approach suffers from performance issues because the run time is directly proportional to the number of people in the image. The method used in the paper uses a nonparametric representation called Part Affinity Fields (PAFs). This approach uses the location and orientation of body parts to encode unstructured pairwise relationships between the body parts of the individuals in the image. In the earlier version of the same paper, they refined both the confidence maps and the part affinity fields (PAFs) at each stage using a branched multistage Convolutional neural network (CNN) and this resulted in much more computation and time at each stage. [1] This paper focused on refining the existing techniques and the architecture of the CNN [1]. They found that PAF refinement is crucial for maximising accuracy, while body part prediction refinement is not that important. In the paper, the author used the first 10 layers of the VGG-19 model as a baseline network to extract feature maps. These feature maps are input to the model to derive the PAFs that define the degree of association between the parts and the confidence maps, which are then refined using the greedy approach. The paper OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields is published by Zhe Cao, Student Member, IEEE, Gines Hidalgo, Student Member, IEEE, Tomas Simon, Shih-En Wei, and Yaser Sheikh under IEEE Transactions on Pattern Analysis and Machine Intelligence journal. Summary of the work, results, and findings In the first stage, the feature maps of the input image are processed by passing it through a baseline network. In this paper, the author uses the first 10 layers of the VGG-19 model for generating the feature maps. Then the feature maps are processed with multiple stages of CNN to generate a set of Part Confidence Maps and a set of Part Affinity Fields. The system uses a multi-stage CNN. It consists of two types of stages. The first set of stages predicts part affinity fields i.e., degree of associations between the body parts the last set of stages predicts the confidence maps. By replacing each 7x7 convolutional kernel with 3 consecutive 3x3 kernels, the current model was able to preserve the receptive field while computation expense is reduced. Using an approach like DenseNet [11] the output of the 3 convolution kernels is concatenated. This approach tripled the non-linearity layers and helped the model to keep both higher and lower-level features. A loss function is applied at the end of every step in both branches. Finally, the Confidence Maps and Part Affinity Fields are processed by a greedy algorithm where optimal partite graph matching is used to obtain the poses for each person in the image. The results and findings are presented, and the accuracy of the multi-person keypoint detection has been compared to the existing methods [2][3]. The real-time performance metric for the system has been specified by mentioning the number of frames it can process each second. All findings are visualized, and graphs are provided for better understanding. The author finds that the newly refined approach increased both the speed and accuracy by approximately 200% and 7% respectively. The paper contributed to the field of foot detection by presenting an annotated foot dataset with 15K human foot instances that have been publicly released for further study and development. The author open-sourced their work and the OpenPose system provides easy-to-use pipelines with command-line interfaces, python API, Unity plugin. It has also been included in the OpenCV library for ease of access. The system runs on CPUs and GPUs such as CUDA and OpenCL. They also provide portable executable binaries which can easily be downloaded and run with minimal setup. Pose-ShuffleNet is a lightweight multi-person pose estimation method developed by integrating the concepts of this paper [4]. This method is highly suited for resource-constrained scenarios. It is based on the building blocks of the ShuffleNet unit [5] for image analysis in place of VGG-19. An architecture of multi-stages with two branches, jointly learning parts detection and parts association has been used. Pose-ShuffleNet has a significantly lower resource (network size and FLOPs) consumption than some of the available leading methods. Using the concepts of OpenPose the research has been further extended to the 3D human pose estimation [6]. Further research on Intention Recognition of Pedestrians and Cyclists by 2D Pose Estimation has been carried out. Overall, the proposed pipeline provides promising results on the intention recognition of vulnerable road users. [7] Pros and Cons of the paper: Explanation with pictorial references clarifies the topic in a better way and was effective in clarifying relationships, simplifying complex topics, and was one of the main key factors that had me inclined toward the paper. All the edge cases have been acknowledged and future actions that can mitigate the failure cases have also been suggested. The accuracy and performance of the combined model with body and foot key points is also an important contribution to the paper. The generality of the method by applying it to the task of vehicle key point estimation and the use of the approach has contributed to the wide applications and competitive performance of the model. The paper also showcased three benchmarks performed on reputed and easily available datasets such as MPII [9], COCO [10], and a subset of COCO used as a foot dataset. And provide runtime analysis comparison against Mask R-CNN and Alpha-Pose to quantify the efficiency of the system and analyses the main failure cases. The model performs up to 3 times faster on GPUs when compared to CPUs and in the case of the body and foot model the performance difference is up to 30 times. Making the system more optimised on CPUs can help to make the model more accessible in smaller and less powerful systems and increase the use cases. [8] Potential Advancement and Future Work There have been many advancements in the field of pose estimation, and it is being used in various fields and applications, but at present, the practical applications are limited due to the challenges of crowded people and occluded body parts. A hybrid system of both top-down and bottom-up approaches along with Using anthropometric ratios for determining context can make the model more efficient [7]. Future developments can be made to generate a similar performance on CPUs using lightweight CNNs such as MobileNetV1, EfficientNet or a combination and can help the system be more efficient so that it is less reliant on GPUs and work on embedded systems. It can have various applications in security cameras, child monitors, automating warehouses, gesture control etc. Data plays a key role in improving the robustness of the network. Adding complex scenarios of human poses along with samples of human statues and paintings would boost the accuracy by avoiding the problem of occluded body parts. Improvement in the greedy approach can help to make the pose estimation more accurate. The greedy approach used in the current system suffers from limited structural context when subjects are occluded. Also, the Hungarian algorithm [8] employed is having a cubic complexity. By using a larger structural context of a subset of pre-assigned parts rather than using only the immediate predecessors as in [7], this time complexity can become linear in the number of candidates of any single part-class. Refining the algorithm to reject less likely or complex poses and adding an animal dataset to train the system can have wide applications in pest control, animal farming, analysis of the bees as shown in Honeybee Detection and Pose Estimation using Convolutional Neural Networks [10]. References [1] Cao, Zhe, et al. "Realtime multi-person 2d pose estimation using part affinity fields." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. [2] Fang, Hao-Shu, et al. "Rmpe: Regional multi-person pose estimation." Proceedings of the IEEE international conference on computer vision. 2017. [3] He, Kaiming, et al. "Mask r-cnn." Proceedings of the IEEE international conference on computer vision. 2017. [4] Guan, Chen-zhi. "Realtime multi-person 2d pose estimation using shufflenet." 2019 14th International Conference on Computer Science & Education (ICCSE). IEEE, 2019. [5] Zhang, Xiangyu, et al. "Shufflenet: An extremely efficient convolutional neural network for mobile devices." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018. [6] Schwarcz, Steven, and Thomas Pollard. "3d human pose estimation from deep multi-view 2d pose." 2018 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. [7] Fang, Zhijie, and Antonio M. López. "Intention recognition of pedestrians and cyclists by 2d pose estimation." IEEE Transactions on Intelligent Transportation Systems 21.11 (2019): 4773-4783. [8] Osokin, Daniil. "Real-time 2d multi-person pose estimation on cpu: Lightweight openpose." arXiv preprint arXiv:1811.12004 (2018). [9] Andriluka, Mykhaylo, et al. "2d human pose estimation: New benchmark and state of the art analysis." Proceedings of the IEEE Conference on computer Vision and Pattern Recognition. 2014. [10] Rodríguez, I. F., et al. "Honeybee detection and pose estimation using convolutional neural networks." Congres Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP). 2018. [11] Huang, Gao, et al. "Densely connected convolutional networks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017. 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Reviewing “OpenPose - Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields”

Integrating North American Credit Models into Asian and Middle Eastern Educational Systems

Solos: A Dataset for Audio-Visual Music Analysis-  Conclusions and References

সোলোস: অডিও-ভিজ্যুয়াল মিউজিক অ্যানালাইসিসের জন্য একটি ডেটাসেট- উপসংহার এবং রেফারেন্স

Solos: Ein Datensatz für audiovisuelle Musikanalyse – Schlussfolgerungen und Referenzen

Solos: un conjunto de datos para el análisis de música audiovisual: conclusiones y referencias

Solos : un ensemble de données pour l'analyse de la musique audiovisuelle - Conclusions et références

सोलोस: ऑडियो-विज़ुअल संगीत विश्लेषण के लिए एक डेटासेट- निष्कर्ष और संदर्भ

Solos: オーディオビジュアル音楽分析のためのデータセット - 結論と参考文献

솔로: 시청각 음악 분석을 위한 데이터 세트 - 결론 및 참고 자료

Solos: um conjunto de dados para análise musical audiovisual - conclusões e referências

Соло: набор данных для аудиовизуального анализа музыки – выводы и ссылки

Sololar: Görsel-İşitsel Müzik Analizi için Bir Veri Kümesi - Sonuçlar ve Referanslar

Solos: Bộ dữ liệu để phân tích âm nhạc nghe nhìn- Kết luận và tài liệu tham khảo

Solos：视听音乐分析数据集 - 结论和参考文献

Authors:
(1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu};
(2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu};
(3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro
Related work
Dataset
Experiments
Conclusions and References III. DATASET Solos[1] was designed to have the same categories as the URMP [1] dataset, so that URMP can be used as testing dataset in a real-world scenario. This way we aim to establish a standard way of evaluating source separation algorithms’ performance avoiding the use of mix-and-separate in testing. Solos consists of 755 recordings distributed amongst 13 categories as shown in Figure 1, with an average amount of 58 recordings per category and an average duration of 5:16 min. It is interesting to highlight that, for 8 out of 13 categories, the median of resolution is HD, despite being a YouTube-gathered dataset. Per-category statistics can be found in Table I. These recordings were gathered by querying YouTube using the tags solo and auditions in several languages such as English, Spanish, French, Italian, Chinese or Russian. A. OpenPose Skeletons Solos is not only a set of recordings. Apart from the videos identificators We also provide: i) body and hand skeletons estimated by OpenPose [33] in each frame of each recording and ii) timestamps indicating useful parts. OpenPose is a system capable to predict body skeleton and hands skeletons making use of two different neural networks. To do so, they predict a confidence map of the belief that a specific body part may be located at any given pixel as well as part affinity fields which encode the degree of association between different body parts. Finally, it predicts 2D skeletons and per-joint confidence via greedy inference. In practice, the body skeleton is estimated with a first network. Then, the position of the wrists in the body skeleton are used to estimate the position of both hands. A second neural network obtains the skeleton of each hand independently. Note that since each body part is estimated independently, OpenPose makes no assumptions about the limbs to find. It just calculates the most likely skeleton given confidence maps and part affinity fields. The whole process is carried out frame-wise. This leads to a small flickering and mispredictions between frames. B. Timestamps estimation and skeleton refinement OpenPose maps mispredicted joints to the origin of coordinates. We empirically found that such a big jump in the position of a joint induces noise. Using interpolated coordinates helps to address this problem. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. [1] Dataset available at https://juanfmontesinos.github.io/Solos/ Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Authors: Authors: (1) Juan F. Montesinos, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {juanfelipe.montesinos@upf.edu}; (2) Olga Slizovskaia, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {olga.slizovskaia@upf.edu}; (3) Gloria Haro, Department of Information and Communications Technologies Universitat Pompeu Fabra, Barcelona, Spain {gloria.haro@upf.edu}. Table of Links Abstract and Intro Related work Dataset Experiments Conclusions and References Abstract and Intro Abstract and Intro Related work Related work Dataset Dataset Experiments Experiments Conclusions and References Conclusions and References III. DATASET Solos[1] was designed to have the same categories as the URMP [1] dataset, so that URMP can be used as testing dataset in a real-world scenario. This way we aim to establish a standard way of evaluating source separation algorithms’ performance avoiding the use of mix-and-separate in testing. Solos consists of 755 recordings distributed amongst 13 categories as shown in Figure 1, with an average amount of 58 recordings per category and an average duration of 5:16 min. It is interesting to highlight that, for 8 out of 13 categories, the median of resolution is HD, despite being a YouTube-gathered dataset. Per-category statistics can be found in Table I. These recordings were gathered by querying YouTube using the tags solo and auditions in several languages such as English, Spanish, French, Italian, Chinese or Russian. A. OpenPose Skeletons A. OpenPose Skeletons Solos is not only a set of recordings. Apart from the videos identificators We also provide: i) body and hand skeletons estimated by OpenPose [33] in each frame of each recording and ii) timestamps indicating useful parts. OpenPose is a system capable to predict body skeleton and hands skeletons making use of two different neural networks. To do so, they predict a confidence map of the belief that a specific body part may be located at any given pixel as well as part affinity fields which encode the degree of association between different body parts. Finally, it predicts 2D skeletons and per-joint confidence via greedy inference. In practice, the body skeleton is estimated with a first network. Then, the position of the wrists in the body skeleton are used to estimate the position of both hands. A second neural network obtains the skeleton of each hand independently. Note that since each body part is estimated independently, OpenPose makes no assumptions about the limbs to find. It just calculates the most likely skeleton given confidence maps and part affinity fields. The whole process is carried out frame-wise. This leads to a small flickering and mispredictions between frames. B. Timestamps estimation and skeleton refinement B. Timestamps estimation and skeleton refinement OpenPose maps mispredicted joints to the origin of coordinates. We empirically found that such a big jump in the position of a joint induces noise. Using interpolated coordinates helps to address this problem. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv [1] Dataset available at https://juanfmontesinos.github.io/Solos/

Solos: A Dataset for Audio-Visual Music Analysis -  Dataset

সোলোস: অডিও-ভিজ্যুয়াল সঙ্গীত বিশ্লেষণের জন্য একটি ডেটাসেট - ডেটাসেট

Solos: Ein Datensatz für die audiovisuelle Musikanalyse - Datensatz

Solos: un conjunto de datos para el análisis de música audiovisual - Conjunto de datos

Solos : un ensemble de données pour l'analyse de la musique audiovisuelle - Ensemble de données

सोलोस: ऑडियो-विज़ुअल संगीत विश्लेषण के लिए एक डेटासेट - डेटासेट

Solos: オーディオビジュアル音楽分析のためのデータセット - データセット

Solos: 시청각 음악 분석을 위한 데이터세트 - 데이터세트

Solos: um conjunto de dados para análise musical audiovisual - conjunto de dados

Соло: набор данных для аудиовизуального анализа музыки - Набор данных

Sololar: Görsel-İşitsel Müzik Analizi için Bir Veri Kümesi - Veri Kümesi

Solos: Bộ dữ liệu để phân tích âm nhạc nghe nhìn - Bộ dữ liệu

Solos：用于视听音乐分析的数据集 - 数据集

Plunging Into Data: Unraveling Time Series Patterns

How Understanding CPU Caches Can Supercharge Your Code: 461% Faster MatMul Case Study

The world of work is changing––rapidly. Thanks to the advent and rapid adoption of generative AI, tech stocks had a bumper 2023, with the share price of the Big Five ( , , , , and ) leaping by 60% from the start of that year. 2023 was also a , which saw its shares triple in price. Alphabet Amazon Apple Meta Microsoft smash success for chipmaker, Nvidia And while adoption isn’t even across the board yet, corporate America is sitting up and taking notice. At JPMorgan Chase, for example, the company said it had 600 machine-learning engineers, with AI working on more than 300 different internal applications. A recent found that 71% of C-suite leaders said that advancing their use of AI is either a high or medium priority for their organization, with 62% of executives noting that they are worried they’re not moving fast enough with AI. study from UKG That has led to some companies doing “AI washing”, aka exaggerating the amount of AI technology they use in their products or services. 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HackerNoon Job Board DataAnnotation is seeking a to train its AI chatbots to code. This flexible, part-time or full-time role allows you to choose the projects you want to work on, involves coming up with diverse problems and solutions for a coding chatbot, writing high-quality answers and code snippets, and evaluating the code quality produced by the AI model. You will need to be proficient in at least one programming language and able to solve coding problems (LeetCode, HackerRank). Software Developer Adobe is looking for a in San Jose. The salary range for this position is $135,200 - $250,900, and you will have either research or industry experience in building scalable data and ML platforms. In this role, you will have the opportunity to build a new GenAI foundation to change the creative landscape and will collaborate with data platform engineers and architects to seamlessly integrate low latency data pipelines into the ML platform for model training, as well as writing high quality, product level code that is easy to maintain and test following standard methodologies. Other tasks include designing and implementing reusable and scalable data-loading framework to support video and audio foundational model training in large-scale and distributed environments. Machine Learning Engineer Python programmers looking for a new role can consider this role at SAIC in Chantilly. You’ll be experienced, results-oriented, and mission-driven, with a specialized focus on data engineering in order to perform data model design, data formatting, and ETL development optimized for efficient storage, access, and computation in support of national security objectives. A solid understanding and experience with developing, automating, and enhancing all parts of the data pipeline to include ingestion, processing, storage, and exposing data for consumption is required, along with a focus on improving inefficient tooling and adopting new transformative technologies while maintaining operational continuity. Python Developer Want a great new tech role? Discover it now on the HackerNoon Job Board right By Kirstie McDermott

MIT Has Released a Bunch of Free Courses: Discover 5 To Boost Your Tech Career

এমআইটি একগুচ্ছ বিনামূল্যের কোর্স প্রকাশ করেছে: আপনার কারিগরি ক্যারিয়ারকে উন্নত করতে 5 আবিষ্কার করুন

Das MIT hat eine Reihe kostenloser Kurse veröffentlicht: Entdecken Sie 5, um Ihre Tech-Karriere voranzutreiben

El MIT ha lanzado una serie de cursos gratuitos: descubra 5 para impulsar su carrera tecnológica

Le MIT a publié un certain nombre de cours gratuits : découvrez-en 5 pour booster votre carrière technologique

एमआईटी ने कई मुफ्त पाठ्यक्रम जारी किए हैं: अपने तकनीकी करियर को बढ़ावा देने के लिए 5 पाठ्यक्रमों के बारे में जानें

MIT が無料コースを多数公開: テクノロジーのキャリアを伸ばす 5 つのコースをご紹介します

MIT가 다양한 무료 강좌를 출시했습니다: 기술 경력을 향상하는 5가지를 찾아보세요

O MIT lançou vários cursos gratuitos: descubra 5 para impulsionar sua carreira tecnológica

MIT выпустил несколько бесплатных курсов: откройте для себя 5, которые помогут улучшить вашу техническую карьеру

MIT Bir Çok Ücretsiz Kurs Yayımladı: Teknoloji Kariyerinizi Yükseltecek 5 Kursu Keşfedin

MIT đã phát hành một loạt khóa học miễn phí: Khám phá 5 khóa học để thúc đẩy sự nghiệp công nghệ của bạn

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MinIO is frequently used to store data from logging, metrics and trace data whether it be ElasticSearch, OpenTelemetry, OpenSearch, OpenObserve or any of the other dozen or so great monitoring solutions. MinIO is more efficient when used with storage tiering, which decreases total cost of ownership for the data stored, plus you get the added benefits of writing data to MinIO that is , and protected by . In addition, saving data to MinIO object storage makes it available to other cloud native machine learning and analytics applications. immutable versioned erasure coding Quickwit and MinIO share a lot of the same principles. Quickwit is designed for sub-second search straight from object storage allowing true decoupled compute and storage. This means you can store your data on cheap commodity hardware, while MinIO handles the and Integrity of the data. As your needs and requirements change, you scale out your cluster as needed. Quickwit has concepts of Tenants similar to MinIO that are easily isolated and can manage their individual usage. Replication In today’s post we’ll show you how to setup MinIO and Quickwit with a specific focus on Configuring MinIO as a storage provider for Quickwit Set up MinIO as a metadata store for Quickwit Installing MinIO In a previous blog we discussed how to configure service. We’ll use the same principles here except instead of a binary it will be installed as an OS package. MinIO as a SystemD Install the MinIO .deb package. If you are using another OS family you can find other packages here root@aj-test-1:~# wget https://dl.min.io/server/minio/release/linux-amd64/archive/minio_20231120224007.0.0_amd64.deb -O minio.deb

root@aj-test-1:~# dpkg -i minio.deb Create a user and group and , respectively minio-user minio-user root@aj-test-1:~# groupadd -r minio-user
root@aj-test-1:~# useradd -M -r -g minio-user minio-user Create the data directory for MinIO and set the permissions with the user and group created in the previous step root@aj-test-1:~# mkdir /opt/minio

root@aj-test-1:~# chown minio-user:minio-user /opt/minio Enable and Start MinIO service root@aj-test-1:~# systemctl enable minio

root@aj-test-1:~# systemctl start minio You can verify MinIO is running either through the console by going to or through mc admin http://localhost:9001 root@aj-test-1:~# wget https://dl.min.io/client/mc/release/linux-amd64/mc

root@aj-test-1:~# chmod +x mc

root@aj-test-1:~# mv mc /usr/local/bin/mc

root@aj-test-1:~# mc alias set local http://127.0.0.1:9000 minioadmin minioadmin

root@aj-test-1:~# mc admin info local
●  127.0.0.1:9000
   Uptime: 5 minutes
   Version: 2023-11-25T07:17:05Z
   Network: 1/1 OK
   Drives: 1/1 OK
   Pool: 1

Pools:
   1st, Erasure sets: 1, Disks per erasure set: 1

1 drive online, 0 drives offline If you see messages similar to these, you can be assured that MinIO has started. Now we’ll create a bucket and later some objects using Quickwit. root@aj-test-1:~# mc mb local/quickwit

Bucket created successfully `local/quickwit`. Now we are ready to install Quickwit and configure it with MinIO as the backend. Configure Quickwit The Quickwit installer automatically picks the correct binary archive for your environment and then downloads and unpacks it in your working directory. In this case since we are running Ubuntu it will install packages related to that OS but it supports all the popular distributions. curl -L https://install.quickwit.io | sh

cd ./quickwit-v*/
./quickwit --version Curl the configuration file and let's modify it to add the MinIO bits. curl -o quickwit.yaml
https://github.com/quickwit-oss/quickwit/blob/main/config/quickwit.yaml Open the yaml and first add the credentials to configure MinIO storage:
   s3:
     flavor: minio
     access_key_id: minioadmin
     secret_access_key: minioadmin
     endpoint: http://127.0.0.1:9000 Next we’ll add the Storage and Metadata store configurations default_index_root_uri: s3://quickwit/indexes metastore_uri: s3://quickwit/indexes Once the above configurations are set in the YAML, save it and close. In order to use it set it as an environment variable and run the service export QW_CONFIG=./quickwit.yaml ./quickwit run We can check if its working by browsing the UI at or doing a GET http://localhost:7280 curl http://localhost:7280/api/v1/version Let's create an index configured to receive Stackoverflow posts. You need to create an index configured with a YAML to map your input documents to your index fields and whether these fields should be stored and indexed. curl -o stackoverflow-index-config.yaml 
https://raw.githubusercontent.com/quickwit-oss/quickwit/v0.6.4/config/tutorials/stackoverflow/index-config.yaml Once the index is downloaded create it ./quickwit index create --index-config ./stackoverflow-index-config.yaml To hydrate the index we just created, we’ll download a sample of the first 10,000 Stackoverflow posts and then feed this data into Quickwit which will store it on MinIO in the backend. curl -O https://quickwit-datasets-public.s3.amazonaws.com/stackoverflow.posts.transformed-10000.json ./quickwit index ingest --index stackoverflow --input-path stackoverflow.posts.transformed-10000.json --force As soon as the ingest command finishes you can start querying data by using the command search ./quickwit index search --index stackoverflow --query "search AND engine" You can use more advanced features such as aggregations like the following query to find the most popular tags used on the questions in this dataset curl -XPOST "http://localhost:7280/api/v1/stackoverflow/search" -H 'Content-Type: application/json' -d '{
    "query": "type:question",
    "max_hits": 0,
    "aggs": {
        "foo": {
            "terms":{
                "field":"tags",
                "size": 10
            }
        }
    }
}' Final Thoughts MinIO is the right choice for Quickwit because of its industry-leading performance and scalability. MinIO’s combination of scalability and high-performance puts every data-intensive workload, not just Quickwit, within reach. MinIO is capable of tremendous performance - a achieved 325 GiB/s (349 GB/s) on GETs and 165 GiB/s (177 GB/s) on PUTs with just 32 nodes of off-the-shelf NVMe SSDs. This makes managing Quickwit with MinIO seamless for log management, distributed tracing, and immutable data such as conversational data, event-based analytics among others. recent benchmark By storing the data in MinIO, Quickwit can be used as a Grafana datasource for achieving fast visibility into the operations of your application. You can see patterns and set alerts in Grafana's graphical interface that would allow you to run historical analysis and act on anomalies based on certain thresholds. For example, you might want to check for trends or bottlenecks and try to identify patterns in workload type during a specific time of the day. Got questions? Want to get started? Reach out to us on . Slack Also published . here

Setting Up MinIO With Quickwit

Quickwit এর সাথে MinIO সেট আপ করা হচ্ছে

MinIO mit Quickwit einrichten

Configurar MinIO con Quickwit

Configuration de MinIO avec Quickwit

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