Questions tagged [bigdata]

Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization.

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How are big models like GPT-3 developed?

For models trained with a moderate amount of data, I would expect a the development workflow to include extensive hyper-parameter optimization through extensive retraining. But how is this handled for ...
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Ideas on handling large quantities of “waste” data

Hello and thank you for taking time out of your day to help me. I am currently working on developing a machine vision application for production monitoring. The application handles images, about 20,...
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What are best practices when exploring streaming data? [closed]

I'm interested to learn if this community has best practices when dealing with large, streaming data. I'm in a situation where I'm working without an event dictionary, so I'm "going with the (...
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How to store efficiently very large sparse 3D matrices

To train a CNN, I have stacked arrays of images over observations [observations x width x length]. The dataset is very sparse ($95\%$). What would be an efficient ...
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Create User Table from Google Firebase Analytics and Cloud Firestore Collections

I am exporting my snowplow collections collected on Firebase using the extention Export Collections to BigQuery firestore-bigquery-export v0.1.10. Whenever a document is created, updated, imported, or ...
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How does skewed data affect deep neural networks?

I'm playing around with deep neural networks for a regression problem. The dataset I have is skewed right and for a linear regression model, I would typically perform a log transform. Should I be ...
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Public dataset for news articles with their associated categories for multilabel data classification

I am wondering if there are any public datasets of news, like New York Times (NYT) or similar to various news categories such as politics, entertainment, lifestyle, general news, sports etc. I want to ...
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How much information is produced in our Society? [closed]

I was reading an interesting article about the growth of data in our society: https://rss.onlinelibrary.wiley.com/doi/epdf/10.1111/j.1740-9713.2012.00584.x Does any of you know if there exists a more ...
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Automatic EDA for BIG Data

I have data set of cicids2017 which in total has 2830743 rows and 79 columns. Since my local jupyter was crashing a lot, SO I am using Google Colab. But Google Colab is also crashing if some big ...
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What is the main difference between Hadoop and Spark? [closed]

I recently read the following about Hadoop vs. Spark: Insist upon in-memory columnar data querying. This was the killer-feature that let Apache Spark run in seconds the queries that would take Hadoop ...
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Looking for the proper algorithm to compress many lowres images of nearby locations

I have an optimization problem that I'm looking for the right algorithm to solve. What I have: A large set of low-res 360 images that were taken on a regular grid within a certain area. each of these ...
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How can I predict the true label for data with incomplete features based on the model learned by data with complete features? [closed]

for example, the model was learned by training data with complete features (f1,f2,f3,f4,f5,f6) but, I wonder the model can test data with incomplete features (f1,f2,f3) to attach true label into these ...
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Does Call Detail Record(CDR) data record different cell-site/tower on incoming and outgoing call/text?

I am working on CDR data to find important locations of user. The data I have does not have coordinates so I will be using cell-site location as user's location. But after doing some working on data I ...
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How to determine sample rate of a time series dataset?

I have a dataset of magnetometer sensor readings which looks like: ...
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How do I efficiently load data from disk during training of deep learning models in pytorch?

I'm trying to train a deep learning model without loading the entire dataset into memory. My main question is, what's the best way of doing this? It seems like HDF5 is a common method that people ...
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Spark: How to run PCA parallelized? Only one thread used

I use pySpark and set my configuration like following: ...
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What is important for Pharmaceutical companies to answer with Big Data Analysis?

I am a data scientist, and I have some biological background (genetics). I have been asked to give a talk for our customers from pharmaceutical industry. I should show them how they benefit from Big ...
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Maximum number of images to train a convolutional neural network

I just wondered if there is a technical limit on the number of images to train a neural network. I want to work with extremely high numbers of images, around 1,000,000 to 10,000,000 images. Is there ...
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Training models from sklearn using tf.distribute.MirroredStrategy

I want to distribute the training of a simple model, such as a support vector classifier like sklearn.svm.SVC() across some or all CPUs and GPUs on a single device. I have never utilized a GPU before ...
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Suggestion of dataset

I am implementing my own deep network, but I am not so good at calculus so my network only works for binary data in the moment. I have been searching for big tabular datasets that are for binary ...
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What makes a good image dataset to compare biological NNs to ANNs?

I'm going to be comparing biological NNs to ANNs based on compounded adversarial attacks, but wish to know what makes a good image dataset to test on biological NNs? Going for image classification. I'...
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anomaly detection in vehicle sensor data

I am currently diving deeper into understanding more about anomaly detection in regards to vehicle's data generated by sensors. It seems like there is no proper book or article that goes deeper into ...
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How to feed key-value features (aggregated data) to LSTM?

I have the following time-series aggregated input for an LSTM-based model: ...
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Surpassing MongoCompass limit() limitations

I have a cluster containing aproximately 8 million files in json format(tweets). My problem is that when analyzing the schema, I can only visualize 1000 files, no matter what number i enter in the ...
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When training NN, how do data loaders work on large datasets?

How do you normally organize large data-sets for easy loading when training Neural Networks? I have a largeish data-set which cannot fit into memory, it consists of 200,000 samples, with 10k samples ...
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Using lmer in R Studio with a large data set

I have a very large data set (127,000 observations, each point has 22 variables). I am only interested in 7 of those variables. The two dependent variables (linear and rotational acceleration) are ...
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Evaluate new store location using customer movement data

I'm trying to build a decision support model for a brand to help in deciding where to open a new store of the brand in an urban area. The model will be focused on location observations (lat-lng, ...
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How to use data provided in EEM20 forecasting competition?

I am new to competitions. I have gone through few Kaggle competitions and most of the time, they provide train data, test data and other supplementary data. Recently, I came across a new competition (...
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Creating more than one worker nodes for local windows machine [closed]

I am using windows laptop. And I installed apache spark for my laptop. And I try to measure spark performance by changing spark components. because of that I want to create more than one worker nodes ...
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Alternatives to reshaping the data

I have a medical dataset which looks like this: ...
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37 views

SGDClassifier on Big Data

I am trying actually to train a SGDClassifier with over 4,000,000 samples of data without any positive results. X vector has 6 features and looks like : [ 2 , 4 , 56431555 , 1 , 0 , 33] Y vector has ...
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How to run Spark python code in Jupyter Notebook via command prompt

I am trying to import a data frame into spark using Python's pyspark module. For this, I used Jupyter Notebook and executed the code shown in the screenshot below After that I want to run this in CMD ...
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135 views

Dask error when reading data from a large zip file

I have a .zip folder with a lot of .csv files which I read into a Dask DataFrame like this: ...
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Basket items optimisation minimising constraints

I have a real problem (not home work) when I have to distribute an ordered list by position to respect some constraints eg. 1. 11 2. 15 3. 18 4. 18 5. 1 baskets:...
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Loading file into and out of HDFS via system call/cmd line vs using libhdfs

I am trying to implement a simple C/C++ program for the HDFS file system like word count, it takes a file from the input path puts it into HDFS (where it gets split), processed my map-reduce function ...
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Approximate evaluation for deep learning architecture

When train on big dataset for deep learning architecture, like imagenet, it takes long time to judge whether our new neural network architecture is good, say for image classification. Is there a way ...
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2answers
51 views

Is there an unsupervised learning algorithm that can cluster data based on more than two dimensions?

I am just beginning to get into data science and have never posted here before, apologies if this question is worded incorrectly! I am curious if there is an unsupervised machine learning algorithm ...
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Scalable test-to-control matching method needed for Amazon cloud environment

I'm working on a project to port some of my employer's processes from our local Unix servers to the Amazon cloud. One process matches records from a test group to records from a control group using ...
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NearestNeighbors testing

I have used nbrs = NearestNeighbors(metric= 'cosine', algorithm='brute').fit(items_features) distances, indices = nbrs.kneighbors(item_features) to find some ...
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107 views

Multiprocessing or Parallel Computing Python Code

I am working on bioinformatics big datasets; training set and optimisation taking huge time to execute. I check and found that training and optimisation compiling on one core of cpu and because of ...
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1answer
155 views

Comparing one small dataset with a big dataset for similar records

I create a varying small dataset (dataset: X) with 500 records in each query. Everytime I need to compare the dataset with a bigger one (dataset: A) (15 milion records) to find similar (or semi-...
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Project structure ML time-series forecasting

I'm about to start with a ML learning time-series forecasting project which includes large-quantities of (2-million) time-series stored in JSON-files. The first step will involve feature engineering....
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Advantage of Centralized training in deep learning over distributed training

I know distributed training is the ultimate solution for scalability and solving resource constraints and sometimes distributed training outperforms centralized training in terms of accuracy. But I'm ...
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1answer
36 views

How to compute modulo of a hash?

Let's say that I have a set of users in my database, that have GUIDs as their IDs. I use xxhash to generate fixed-length hashes for each value, so that I can then ...
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1answer
50 views

How to separate words that are together in a large data set

in twitter data i came across words that are glued together like 'boycottbears' i want them as 'boycott' 'bears' 'man' i tried this but this is slow ...
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296 views

How do tech-companies employ Random Forest on large data sets?

The algorithm takes quite a long time to train on large data sets with a moderate number of parameters: https://stats.stackexchange.com/questions/37370/random-forest-computing-time-in-r https://...
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Where is the start point if I want to use artificial intelligence on NASA space industry?

It sounds like dump question but as a beginner, working/learning AI and want to understand what artificial intelligence in the NASA space industry is doing. I want to work on this field but I don't ...
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R language - text.tree()'s pretty parameter

running the below R code to perform analysis on RoomOccupancy dataset ...
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1answer
6k views

How to create a new dataframe using the another dataframe

I have created and worked on a DataFrame for a project. It looks like the following: Critics Items Ratings a...........1..........5 b...........2..........3 b...........3..........2 c...........
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Can a machine learning model be trained on Call Detail Record(CDR) Data to predict user's daily locations?

I have a CDR data for two months and my goal is to extract daily or frequent locations(cell towers) of the user along with the departure and arrival time on those locations. The spatial resolution of ...

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