In a data science project, a significant effort is spent pre-processing databases, text and image data sets, before a machine learning model can be computed. Moreover, the analytic process is iterative, requiring a team of data scientists to add and remove features (attributes) from the target data set going all the way back to data sources. However, such programming effort is carried out without a data model behind, which results in redundant and inconsistent data sets and source code difficult to extend and maintain. On the other hand, the ER model has a proven track record to design databases before developing source code. Heeding the importance of data-centric models to store and analyze data, we propose a hybrid diagram (FLOWER=FLOW+ER) mixing data objects and processing flow, under the CRISP standard. Specifically, our novel diagram mixes ER entities (data objects) and processing steps (function calls). We explain how diagram creation can be partially automated by parsing source code. We present two case studies illustrating how our proposed hybrid diagram works in two challenging problems from biomedical engineering and computer vision: (1) detecting and classifying spikes in biomedical signals and (2) labeling identified objects in images. We argue our diagram can reduce code development time, enhance team collaboration and eliminate redundant data. We hope our work will motivate research bridging database design, project management and machine learning.

We discuss our progress towards solving a challenging biomedical problem: identifying similar patterns among multiple physiological nerve signals hidden in high throughput data, collected from micro electrical sensors implanted in several animal organs. The problem is difficult because patterns come as spikes within millisecond time-windows, data sets have high dimensionality and there is background electrical noise. A previous analytic system discovers patterns combining PCA dimensionality reduction and K-means clustering, which is slow and misses important patterns hidden by noise. Moreover, it requires reading the data set several times and it requires multiple languages and tools. With such limitations in mind, we present an improved, integrated system that effectively allows the discovery of more accurate patterns, with automated algorithm parameter tuning, by learning model parameters incrementally exploiting summarization. Our integrated solution combines signal filtering, variable construction (feature engineering) and multidimensional data summarization, for a tighter and more effective integration of PCA and K-means clustering. We present preliminary experiments on signals collected from key nerves in a rat. We show our method discovers more patterns in larger time-windows, with better noise filtering, taking less time. In the future, we plan to link signal patterns to specific physiological functions, paving the way for innovative medical treatment via nerve stimulation.

Incremental learning is used to continuously update and tune an existing model, as more data points come in. However, there is a tradeoff between speed and accuracy as the model becomes stable. From a big data angle, computing machine learning models is challenging when data sets cannot fit in main memory or when they exceed CPU capacity. On the other hand, data summarization is a fundamental technique that has promise to accelerate data science computations and compress a data set. Keeping these motivations in mind, we present an innovative system, VIM, that computes machine learning models in an incremental manner, visualizing continuous learning of model parameters as the data set is scanned. Our system is fast, it works for a wide spectrum of machine learning models and it can handle data sets larger than main memory. We developed an intuitive GUI which: (1) guides the user to upload data sets and choose a machine learning model, (2) offers interactive visualization of model parameters, and (3) helps getting an approximate model, stopping early, without reading the whole data set.