# migrations

dir: embeddings_queue
filename: 00001-embeddings.sqlite.sql
hash: d3755dfd232be8e8301f4d7fcfb3a486
sql:
CREATE TABLE embeddings_queue (
    seq_id INTEGER PRIMARY KEY,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    operation INTEGER NOT NULL,
    topic TEXT NOT NULL,
    id TEXT NOT NULL,
    vector BLOB,
    encoding TEXT,
    metadata TEXT
);


dir: sysdb
filename: 00001-collections.sqlite.sql
hash: 38352d725ad1c16074fac420b22b4633
sql:
CREATE TABLE collections (
    id TEXT PRIMARY KEY,
    name TEXT NOT NULL,
    topic TEXT NOT NULL,
    UNIQUE (name)
);

CREATE TABLE collection_metadata (
    collection_id TEXT REFERENCES collections(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (collection_id, key)
);


dir: sysdb
filename: 00002-segments.sqlite.sql
hash: 2913cb6a503055a95f625448037e8912
sql:
CREATE TABLE segments (
    id TEXT PRIMARY KEY,
    type TEXT NOT NULL,
    scope TEXT NOT NULL,
    topic TEXT,
    collection TEXT REFERENCES collection(id)
);

CREATE TABLE segment_metadata (
    segment_id TEXT  REFERENCES segments(id) ON DELETE CASCADE,
    key TEXT NOT NULL,
    str_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (segment_id, key)
);


dir: metadb
filename: 00001-embedding-metadata.sqlite.sql
hash: 2b4cf52c4bb2676e21d6860a4409f856
sql:
CREATE TABLE embeddings (
    id INTEGER PRIMARY KEY,
    segment_id TEXT NOT NULL,
    embedding_id TEXT NOT NULL,
    seq_id BLOB NOT NULL,
    created_at TIMESTAMP NOT NULL DEFAULT CURRENT_TIMESTAMP,
    UNIQUE (segment_id, embedding_id)
);

CREATE TABLE embedding_metadata (
    id INTEGER REFERENCES embeddings(id),
    key TEXT NOT NULL,
    string_value TEXT,
    int_value INTEGER,
    float_value REAL,
    PRIMARY KEY (id, key)
);

CREATE TABLE max_seq_id (
    segment_id TEXT PRIMARY KEY,
    seq_id BLOB NOT NULL
);

CREATE VIRTUAL TABLE embedding_fulltext USING fts5(id, string_value);


dir: metadb
filename: 00002-embedding-metadata.sqlite.sql
hash: 12a570f7121b3a8ce750a2a7c36da20f
sql:
-- SQLite does not support adding check with alter table, as a result, adding a check
-- involve creating a new table and copying the data over. It is over kill with adding
-- a boolean type column. The application write to the table needs to ensure the data
-- integrity.
ALTER TABLE embedding_metadata ADD COLUMN bool_value INTEGER


dir: metadb
filename: 00003-full-text-tokenize.sqlite.sql
hash: f97ad6334aeaa8f419f01110b648b97a
sql:
CREATE VIRTUAL TABLE embedding_fulltext_search USING fts5(string_value, tokenize='trigram');
INSERT INTO embedding_fulltext_search (rowid, string_value) SELECT rowid, string_value FROM embedding_metadata;
DROP TABLE embedding_fulltext;


# embeddings_queue

created_at: 2023-09-15 23:01:21
topic: persistent://default/default/6183cb9a-e1e5-4a9e-97a7-5c853f61fd3a
id: c8aa9e0c-541b-11ee-842a-acde48001122
encoding: FLOAT32
metadata:
{"source": "documents/Arslan_resume.pdf", "file_path": "documents/Arslan_resume.pdf", "page": 0, "total_pages": 1, "format": "PDF 1.3", "title": "Arslan_resume_WatsonAI", "author": "Arslan", "subject": "", "keywords": "", "creator": "Word", "producer": "macOS Version 10.15.7 (Build 19H2) Quartz PDFContext", "creationDate": "D:20201113081708Z00'00'", "modDate": "D:20201113081708Z00'00'", "trapped": "", "chroma:document": "ARSLAN AHMED http://www.linkedin.com/in/arslan-m-ahmed ; https://github.com/arslan2k12 arslan.m.ahmed@gmail.com +1 (343) 988 7106 WORK EXPERIENCE PATENT/PUBLICATIONS SKILLS EDUCATION \u00d8 Data Scientist \u2013 IBM Toronto Software Lab, Canada Mar 2019 - Present * Collect data from various disparate sources (segment, amplitude, IBM streams) and organize it using various frameworks to make it suitable for analysis. * Design and develop various analytical and machine learning models to extract actionable insights from large and complex datasets (Watson Studio, Jupyter Notebooks, Python, Spark, IBM Analytics Engine, IBM Cloud Functions, Jenkins). * Work with various stakeholders including cross-functional teams (product managers, application developers, data engineers, sales and marketing) to collect and understand their goals/requirements, formulate a complete end-to-end solution, and help them in its deployment and integration to drive long term business value (Gainsight, Braze). * Communicate outcomes and findings to the clients in a clear story telling manner using visualization dashboards (Tableau, Cognos, MS Excel reports and charts). * IBM ambassador. Participated and won various hackathons. IBM certified mentor, teacher and speaker for data science.  \u2022 Customer Success \u2013 Data Science and Insights team: * Developed a statistical model that scores the health of IBM Cloud customers based on their support tickets. Used text processing and feature extraction techniques. Retained 246 customers and gross $2M Monthly Rated Usage. * Analysed historical journey and usage trends of Watson Assistant customers using regression model, and launched email campaign to help targeted set of users. 20% success rate with $130k business impact. * Implemented classification algorithms to prevent PaaS clients from churning. Used historical usage trends along with segmentation and feature engineering. Projected impact $0.3M per year from 55 customers. \u2022 Area 631 incubator (team of 6 from diverse backgrounds work in a startup style for 3 months to create 1 breakthrough): * Lead data architect and backend developer for project Nimbus: a smart recommendation tool for IBM Cloud customers. * Designed and developed a scalable prototype that analysed customers\u2019 historical data (using market basket algorithms and sequence mining techniques) to provide real-time contextual insights (including cross-sell and up-sell) for users to help optimize their current environment. Used IBM Streaming"}


created_at: 2023-09-15 23:01:21
topic: persistent://default/default/6183cb9a-e1e5-4a9e-97a7-5c853f61fd3a
id: c8aa9e70-541b-11ee-842a-acde48001122
encoding: FLOAT32
metadata:
{"source": "documents/Arslan_resume.pdf", "file_path": "documents/Arslan_resume.pdf", "page": 0, "total_pages": 1, "format": "PDF 1.3", "title": "Arslan_resume_WatsonAI", "author": "Arslan", "subject": "", "keywords": "", "creator": "Word", "producer": "macOS Version 10.15.7 (Build 19H2) Quartz PDFContext", "creationDate": "D:20201113081708Z00'00'", "modDate": "D:20201113081708Z00'00'", "trapped": "", "chroma:document": "analysed customers\u2019 historical data (using market basket algorithms and sequence mining techniques) to provide real-time contextual insights (including cross-sell and up-sell) for users to help optimize their current environment. Used IBM Streaming service. \u00d8 Graduate Research Assistant - Sensor Systems and IoT Lab, Carleton University, Canada Jan 2017 \u2013 Feb 2019 Predicted electricity consumption with up to 96% accuracy by applying machine learning and deep learning models on large time-series data (linear regression, random forest, SVR, neural networks, etc.) \u2013 used IBM Watson Studio, Python: Matplotlib, Pandas, Scikit-learn, Keras, TensorFlow. Proposed a novel ensemble modelling approach as a part of my thesis research. \u2022 Trade Secret IP for IBM in August 2020 \u2013 Topic: Digital Sales and Customer Success \u2022 A. Ahmed, K. Arab, Z. Bouida and M. Ibnkahla, \u201cData communication and analytics for smart grid systems,\u201d IEEE International Conference on Communications (ICC), Kansas City, USA, pp. 1-6, May 2018. Frequently used: Python (NumPy, Pandas, Scikit-learn, Keras, TensorFlow, matplotlib, PySpark); IBM Watson Studio Jupyter Notebook, Cloudant NoSQL database, Cloud Object storage, DB2, Jenkins, IBM Analytics Engine, IBM Cloud Functions, Segment, Amplitude, Braze, Gainsight, IBM streams || Basic expertise: SQL, R, RStudio, Node-Red (Javascript), Tableau, Power BI, Cognos || Past experience: Computer Vision using OpenCV, MATLAB, C/C++ on Visual Studio, Android development in JAVA on Eclipse || Familiarity: ETL, NLP, Text Processing MSc (thesis) Electrical and Computer Engineering with Specialization in Data Science Carleton University, Ottawa ON, Canada (in collaboration with Hydro Ottawa Limited) Jan 2017- Dec 2018 CGPA: 3.67/4 Thesis topic: Real-time communication and analysis of electricity data between home and utility for reducing monthly bills. Bachelor of Electrical Engineering (Gold Medal award and Dean\u2019s merit scholarship) National University of Sciences and Technology (NUST), Pakistan     Sep 2012- Sep 2016 CGPA 3.32/4"}


# embedding_metadata

key: chroma:document
string_value:
ARSLAN AHMED http://www.linkedin.com/in/arslan-m-ahmed ; https://github.com/arslan2k12 arslan.m.ahmed@gmail.com +1 (343) 988 7106 WORK EXPERIENCE PATENT/PUBLICATIONS SKILLS EDUCATION Ø Data Scientist – IBM Toronto Software Lab, Canada Mar 2019 - Present * Collect data from various disparate sources (segment, amplitude, IBM streams) and organize it using various frameworks to make it suitable for analysis. * Design and develop various analytical and machine learning models to extract actionable insights from large and complex datasets (Watson Studio, Jupyter Notebooks, Python, Spark, IBM Analytics Engine, IBM Cloud Functions, Jenkins). * Work with various stakeholders including cross-functional teams (product managers, application developers, data engineers, sales and marketing) to collect and understand their goals/requirements, formulate a complete end-to-end solution, and help them in its deployment and integration to drive long term business value (Gainsight, Braze). * Communicate outcomes and findings to the clients in a clear story telling manner using visualization dashboards (Tableau, Cognos, MS Excel reports and charts). * IBM ambassador. Participated and won various hackathons. IBM certified mentor, teacher and speaker for data science.  • Customer Success – Data Science and Insights team: * Developed a statistical model that scores the health of IBM Cloud customers based on their support tickets. Used text processing and feature extraction techniques. Retained 246 customers and gross $2M Monthly Rated Usage. * Analysed historical journey and usage trends of Watson Assistant customers using regression model, and launched email campaign to help targeted set of users. 20% success rate with $130k business impact. * Implemented classification algorithms to prevent PaaS clients from churning. Used historical usage trends along with segmentation and feature engineering. Projected impact $0.3M per year from 55 customers. • Area 631 incubator (team of 6 from diverse backgrounds work in a startup style for 3 months to create 1 breakthrough): * Lead data architect and backend developer for project Nimbus: a smart recommendation tool for IBM Cloud customers. * Designed and developed a scalable prototype that analysed customers’ historical data (using market basket algorithms and sequence mining techniques) to provide real-time contextual insights (including cross-sell and up-sell) for users to help optimize their current environment. Used IBM Streaming


key: chroma:document
string_value:
analysed customers’ historical data (using market basket algorithms and sequence mining techniques) to provide real-time contextual insights (including cross-sell and up-sell) for users to help optimize their current environment. Used IBM Streaming service. Ø Graduate Research Assistant - Sensor Systems and IoT Lab, Carleton University, Canada Jan 2017 – Feb 2019 Predicted electricity consumption with up to 96% accuracy by applying machine learning and deep learning models on large time-series data (linear regression, random forest, SVR, neural networks, etc.) – used IBM Watson Studio, Python: Matplotlib, Pandas, Scikit-learn, Keras, TensorFlow. Proposed a novel ensemble modelling approach as a part of my thesis research. • Trade Secret IP for IBM in August 2020 – Topic: Digital Sales and Customer Success • A. Ahmed, K. Arab, Z. Bouida and M. Ibnkahla, “Data communication and analytics for smart grid systems,” IEEE International Conference on Communications (ICC), Kansas City, USA, pp. 1-6, May 2018. Frequently used: Python (NumPy, Pandas, Scikit-learn, Keras, TensorFlow, matplotlib, PySpark); IBM Watson Studio Jupyter Notebook, Cloudant NoSQL database, Cloud Object storage, DB2, Jenkins, IBM Analytics Engine, IBM Cloud Functions, Segment, Amplitude, Braze, Gainsight, IBM streams || Basic expertise: SQL, R, RStudio, Node-Red (Javascript), Tableau, Power BI, Cognos || Past experience: Computer Vision using OpenCV, MATLAB, C/C++ on Visual Studio, Android development in JAVA on Eclipse || Familiarity: ETL, NLP, Text Processing MSc (thesis) Electrical and Computer Engineering with Specialization in Data Science Carleton University, Ottawa ON, Canada (in collaboration with Hydro Ottawa Limited) Jan 2017- Dec 2018 CGPA: 3.67/4 Thesis topic: Real-time communication and analysis of electricity data between home and utility for reducing monthly bills. Bachelor of Electrical Engineering (Gold Medal award and Dean’s merit scholarship) National University of Sciences and Technology (NUST), Pakistan     Sep 2012- Sep 2016 CGPA 3.32/4


