![]() ![]() This is pretty complicated, too, so I'll leave you with a relevant paper: Twitter-Network Topic Model: A Full Bayesian Treatment for Social Network and Text Modeling. The textual content, such as it is, won't play nice with these embedding algorithms, but you have hashtags and strong social signals mentions, retweets, and follows. The idea is to attach a number (or rather, a vector) to everything from a word to a document. If all that sounds Chinese to you, start by reading about "named entity recognition", and "word embeddings". If the graph is too dense, thin out the weaker edges. Once you have the embeddings, and the edges (thanks to NER), use a graph layout algorithm like force direction. If I had to do this I would use a named entity recognition (NER) and document embeddings (doc2vec, etc.). There are a lot of things going on here, but k-means is not one of them. You can use IBM SMF Explorer stand-alone in Python scripts or use it with the provided JupyterLab setup.They've created a graph from the news articles, topics, and named entities (locations, persons, companies, organizations). Of course, the tennis watchers of the UK have come to know Berrettini well in the past four weeks. z/OS Explorer is extendable via the IBM repository of compatible products to fulfill each users roles and responsibilities. The crowd fulfilled their half of the bargain: they cheered the gentle giant that is Berrettini to a 6-3, 6-0, 6-7 (3) 6-4 win over Hubert Hurkacz to book his ticket to his first Wimbledon final. It enables the integration of a variety of offerings from IBM and other vendors, and in-house development plug-ins. Thanks to the convenient interface to access SMF data using Python provided by IBM SMF Explorer, you can retrieve SMF data in tabular form, which can further be processed for the task of data analysis and machine learning. IBM Explorer for z/OS (z/OS Explorer) is an Eclipse-based integration platform for z/OS users. Novice users like system programmers, data scientists and data engineers might be struggling when trying to understand and interpret SMF data if they are still not acquainted with z/OS. System Management Facility (SMF) records represent a wealth of information that can be extracted to get insights into the activities of your z/OS systems. Python is relatively easy to learn, and if you are used to scripting languages or programming in general you might find the Tutorial Jupyter Notebooks provided with IBM SMF Explorer sufficient to learn Python on the go.Īdditionally you may find very good publicly available guides and other resources to get you started with Python (e.g. In addition to the setup itself, fundamental Python knowledge and a basic understanding of the Pandas library is helpful to get you started. ![]() There are some technical requirements that are listed below. JupyterLab is a web-based interface to execute Python interactively and makes data visualization and handling easy.įor that reason the IBM SMF Explorer Github repository provides you with a JupyterLab environment to get started quickly. system utilization, LPAR utilization, cache statistics, …).Īn easy way to use IBM SMF Explorer is through JupyterLab. The framework enables easy SMF data record fetching, provides information on selected SMF fields, and serves chunks of SMF data, suitable for different analysis types, that can be selected for further processing.įor various SMF records and subtypes, sets of SMF fields are provided to make getting relevant data even easier (e.g. IBM SMF Explorer is a Python library, which means that you can use it to write scripts, embed it into other applications or just fetch data interactively.Īdditionally the Python ecosystem provides access to a large set of libraries for visualization, data analysis, machine learning and many more. The framework uses the z/OS® Data Gatherer: SMF Data REST Services to fetch data from a z/OS host. IBM® SMF Explorer is a Python framework to access SMF data directly from dump data sets. How to use Mapping and Samples Documentation ![]()
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