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By viewing data as a graph, Ikigai provides DeepMatch, which attempts to learn the relationship between columns of a dataset and use it to further match rows.

Oct 6, 2022
15 minutes to read

Data as a Graph

We view any data as a proxy of an underlying probability distribution — as a mental model, let’s think of data represented as a (potentially giant) spreadsheet with columns of it being (random) variables and rows of it being samples of the (joint probability) distribution over these (random) variables. A generic way to represent any such distribution is to capture it through a graph (or graphical model). In effect, such a graph is like a traditional “data cube” but with a “fuzzy” or “probabilistic” view of it.

Learning such a graph from data can enable all sorts of AI tasks on it including data stitching (aiMatch), AutoML, scenario analysis, and optimization. To enable such functionalities in Ikigai’s low code/no-code platform requires creating such “probabilistic index” in real-time. This needed a technological breakthrough.

Compute with PubSub

At Ikigai, we have achieved this breakthrough by developing novel computational architecture using PubSub. Data as a graph naturally leads to a “message-passing” computational architecture where computationally necessary information is exchanged between units of data (or nodes of a graph). We realized that a computationally scalable way to implement such architecture is by utilizing the classical PubSub infrastructure — what is utilized by modern data bus e.g. Apache Kafka.

In an MIT patent, we outline the architecture to explain how it enables computation to scale with data. In effect, it brings computation to data rather than the traditional approach of bringing data to compute.

What does it enable (and how)?

Ikigai’s Large Graphical Model (LGM) technology is the foundation of its AI-native low-code/no-code AI platform. We describe the functionalities that it enables in a no-code manner that are crucially needed for making Ikigai’s generative AI platform truly useful for a data operator.

aiMatch. The foundational pre-task of most data-driven analysis is that of “stitching” multiple data sources together. Traditionally, in the database language, this is achieved through “joins”. In many modern settings, however, this does not work as they may lack a shared column or have mismatched entries, thus the correct relationship is likely to be missed. In a traditional setting, this results in either a lot of manual work or careful, case-by-case, data processing work.

By viewing data as a graph, Ikigai's aiMatch model attempts to learn the relationship between columns of a dataset and then use it to further match rows. The human-in-the-loop component allows for the end user to provide minimal supervision to correct the inaccuracies (exception is not an exception but a norm when using AI) and to improve the outcome.

aiMatch enables various data reconciliation use cases across a broad range of industries, such as insurance, retail, and finance.. To learn more, see here.

AutoML. The task of prediction can be viewed as that of filling missing values in a spreadsheet. If it is temporal data, it would include imputation prior, historical data, or a forecast of future data. By viewing data as a graph, such tasks can be answered instantly by estimating the missing values, given other data as (conditional) observations.

Leveraging AutoML, users can generate predictions by simply specifying which columns in the data set need to be predicted. Indeed, the predictions come with uncertainty quantification as well as explainability. To learn more about how this enables accurate demand forecasting in retail, see here.

Scenario Analysis. A decision maker needs to weigh various scenarios from the lens of the collection of objectives and constraints to finally make a decision. From long-term strategy to daily tactical level decisions,  scenario analysis enables businesses to select the best plan with any given business constraints. In effect, this requires “simulation” of the future under different decisions. Typically, this is a lot more complicated than simply doing predictions. To learn more about how it enables scenario analysis in the supply chain, see here.

Optimization. Selecting the optimal choice needs to be made across a variety of decision tasks which can be solved by modeling it as a graph. To learn more about how Ikigai enables production planning optimization in supply chain, see here.

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Devavrat Shah

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