One of the challenges in using traditional machine learning techniques is the choice of features. For humans to analyse data and recommend a solution, it generally takes few days. Our approach to such situations is to use an unsupervised technique. We are able to make the machine derive the best features and learn by itself, take feedback and then learn again. Some of the deep learning networks we have used in the past include Restricted Boltzmann Machines, Deep Belief Networks, Convolutional Networks etc. We have successfully used these machines to solve problems such as Image classification, Video classification, Speech recognition, handwriting recognition etc.
We develop systems that solve your complex business problems using our machine learning techniques. With the strong knowledge of algorithms, math geniuses and data engineers put their brains together in identifying the solution and build them to make it awesome. From recommendation engines to spam prediction systems to social media analytics, we have powerful engines built upon ML techniques.
CLOUD BASED ANALYTICS
We offer a slew of cloud-based solutions for small, medium and large enterprises. With zero investment in infrastructure, organizations rely on this robust platform to meet their need for rapid scalability, faster implementation and accessibility anytime-anywhere. Majority of our projects follow a SaaS model whereas we have also executed few PaaS services with elastic infrastructure.
NATURAL LANGUAGE PROCESSING
Our data experts are the best in Natural language processing. We build systems that deals with analyzing, understanding & identifying data from languages that humans use. We have built NLP systems for Advertising, Healthcare & Retail domain.
Natural Language Processing
Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human-computer interaction.
The Apache OpenNLP library is a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, and coreference resolution. These tasks are usually required to build more advanced text processing services.
StanfordNLP is an algorithms that allow computers to process and understand human languages.It covers areas such as sentence understanding, machine translation, probabilistic parsing and tagging, biomedical information extraction, grammar induction, word sense disambiguation, automatic question answering, and text to 3D scene generation.
LingPipe is toolkit for processing text using computational linguistics. LingPipe is used to do tasks like, finding the names of people, organizations or locations in news, Twitter search results into categories and suggest correct spellings of queries. LingPipe’s architecture is designed to be efficient, scalable, reusable, and robust.
GATE is an open source software capable of solving almost any text processing problem. Itt yields a wide range of application in processing voice of the customer, cancer research, drug research, recruitment, web mining, information extraction, semantic annotation etc. Advantages of a GATE-based solution include comprehensiveness, scalability, transparency and robustness.
Natural Language Toolkit (NLTK)
The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English. The main features of NLTK is that it provides easy-to-use interfaces. It has text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning, and an active discussion forum.
Machine learning is a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions.
Waikato Environment for Knowledge Analysis (Weka) is a suite of machine learning software written in Java. Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling. Weka supports several data mining tasks, more specifically data preprocessing, clustering, classification, regression, visualization and feature selection.
Apache Mahout is a machine learning algorithm focused primarily in the areas of collaborative filtering, clustering and classification. Mahout also provides Java libraries for common maths operations (focused on linear algebra and statistics) and primitive Java collections. It also supports Recommendation mining which analyses user’s behavior and predict the items which a user might like.
MLLib is a library for performing machine-learning and associated tasks on massive datasets. With MLlib, fitting a machine-learning model to a billion observations can take a couple lines of code and leverage hundreds of machines. MLlib greatly simplifies the model development process. MLlib currently supports four common types of machine learning problem settings namely binary classification, regression, clustering and collaborative filtering.
CLIPS is a productive development and delivery expert system tool which provides a complete environment for the construction of rule and/or object based expert systems. Key features includes Knowledge Representation, Portability, Extensibility, Interactive Development, Validation and Low Cost. The development of CLIPS has helped to improve the ability to deliver expert system technology throughout the public and private sectors for a wide range of applications and diverse computing environments.
LIBSVM and LIBLINEAR
LIBSVM and LIBLINEAR are two popular open source machine learning libraries, both developed at the National Taiwan University and both written in C++ though with a C API. LIBSVM implements the SMO algorithm for kernelized support vector machines (SVMs), supporting classification and regression. LIBLINEAR implements linear SVMs and logistic regression models trained using a coordinate descent algorithm.
Custom Developed Algorithms
Dexlock helps to solve the client’s challenges they face when identifying gaps in their applications, with Custom Developed Algorithms. Dexlock’s team of experts gets started with an in-depth analysis of a client’s requirements to make an appropriate planned strategy for the tool’s performance and functionality. We will improve user interfaces, test all development and modifications, and fix any bugs and amend product features according to test results.
Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases.
Apache Nutch is an open source Java implementation of a search engine. It provides all of the tools you need to run your own search engine.Nutch is open source so we can access ranking algorithms. Nutch can add search to information of heterogeneous type or can use plugins to add additional functionalities.
An open source and collaborative framework for extracting the data you need from websites. In a fast, simple, yet extensible way. Its fast and powerful framework by writing the rules to extract the data scrapy extracts data from websites. Scrapy is extensible we can add new functionality easily without having to touch the core.
Beautiful Soup is a Python library for pulling data out of HTML and XML files. It provides way for navigating, searching, and modifying the parse tree. It creates a parse tree for parsed pages that can be used to extract data from HTML, which is useful for web scraping.
Large Data Store
The methodology selected to store big data should reflect the application and its usage patterns. Traditional data warehousing operations mined relatively homogenous data sets, often supported by fairly monolithic storage infrastructures in a way that today would be considered less than optimal in terms of the ability to add processing or storage capacity. By contrast, Large data stores processe on huge amount and hetrogenious datas.
Hadoop software library is a framework that allows for the distributed processing of large data sets.It is designed to scale up from single servers to thousands of machines, each offering local computation and storage, Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer.It can store data in raw or any of the serialized formats like Avro, SequenceFile etc.
The Apache Cassandra is a database which provides scalability and high availability without compromising performance. Linear scalability and proven fault-tolerance on commodity hardware or cloud infrastructure make it the perfect platform suited to point lookups and wide tables. Cassandras support for replicating across multiple datacenters makes it a class apart.
Apache HBase is the Hadoop database that can be very useful for range scan based batch processing of records. The main features of HBase database is Linear and modular scalability,Strictly consistent reads and writes,Convenient base classes for backing Hadoop MapReduce jobs with Apache HBase tables and Easy to use Java API for client access.
Aerospike’s Database is Mostly suited for real time queries on large volumes of analytic information. Aerospike is a distributed, scalable NoSQL database, Aerospike’s Java client enables you to build applications in Java that store and retrieve data from an Aerospike cluster. It contains both synchronous and asynchronous calls to the database.
MongoDB & CouchDB
CouchDB uses a document store with data being presented in the JSON format. It offers a RESTful HTTP API for reading, adding, editing, and deleting database documents. The update model for CouchDB is optimistic and lockless. This database structure, inspired by Lotus Notes, can be scaled from global clusters down to mobile devices. MongoDB is schema-free, allowing you to create documents without having to first create the structure for that document.
In Memory Data Storage
An in-memory database (IMDB; also main memory database system or MMDB or memory resident database) is a database management system that primarily relies on main memory for computer data storage. It is contrasted with database management systems that employ a disk storage mechanism.
WhiteDB is a lightweight database which is known for its speed and portability across ecosystems,it operates fully in main memory. Disk is used only for dumping/restoring database and logging. Data is persistently kept in the shared memory area and is available simultaneously to all processes.
Redis is a data structure server. The key features of Redis is that It is open-source, networked, in-memory, and stores keys with optional durability. Data in a key-value database has two parts: the key and the value. Because Redis can accept keys in a wide range of formats, operations can be executed on the server and reduce the client’s workload. Redis is often used for caches to speed up web applications.
HSQLDB and MemSQL
There has been a demand for in memory SQL solutions. Some of the best SQL in memory solutions that we have used include HSQLDB and MEMSQL. These tools are relatively new in the market and see a lot of active development. They have integrations rolled out with almost every other system that might be necessary.
Distributed processing is a phrase used to refer to a variety of computer systems that use more than one computer (or processor) to run an application. This includes parallel processing in which a single computer uses more than one CPU to execute programs.
Apache Giraph is an iterative graph processing system built for high scalability. Giraph adds several features beyond the basic Pregel model, including master computation, sharded aggregators, edge-oriented input, out-of-core computation, and more. Giraph is a natural choice for unleashing the potential of structured datasets at a massive scale.
Apache Spark is an open-source cluster computing framework. Spark uses in-memory primitives which makes performance up to 100 times faster in contrast to Hadoop’s two-stage disk-based MapReduce paradigm.Spark is a real time large data processing system that can use primary memory very effectively.
Hadoop Map Reduce
Hadoop MapReduce is a software framework for distributed processing of large data sets on compute clusters of commodity hardware. MapReduce takes care of scheduling tasks, monitoring them and re-executing any failed tasks.Main feature of MapReduce is the Batch processing of large volumes of data on secondary storage.
The Actor Model provides a higher level of abstraction for writing concurrent and distributed systems. It frees developers from having to deal with explicit locking and thread management, making it easier to write correct concurrent and parallel systems.
Graph Data Store
Database that embraces relationships as a core aspect of its data model is able to store, process, and query connections efficiently. While other databases compute relationships expensively at query time, a graph database stores connections as first class citizens, readily available for any “join-like” navigation operation. Accessing those already persistent connections is an efficient, constant-time operation and allows you to quickly traverse millions of connections per second per core.
Titan is a transactional database that can support thousands of concurrent users executing complex graph traversals in real time.Titan has the ability to use Cassandra/HBase etc for storage and ElasticSearch for indexing. .It is basically a scalable graph processing over big data processing systems.
Graph database capable of holding very large amounts of data. On of the key feature of Neo4j is that programmers works with a flexible network structure of nodes and relationships rather than static tables yet enjoys all the benefits of enterprise quality database.
A pipeline is a set of data processing elements connected in series, where the output of one element is the input of the next one. The elements of a pipeline are often executed in parallel or in time-sliced fashion; in that case, some amount of buffer storage is often inserted between elements.
Apache Kafka is a fast, scalable, durable, and fault-tolerant publish-subscribe messaging system. A single Kafka broker can handle hundreds of megabytes of reads and writes per second from thousands of clients. Kafka is designed to allow a single cluster to serve as the central data backbone for a large organization.
Flume is a Data pipeline for ingress of database it offers service for efficiently collecting, aggregating, and moving large amounts of log data. It has a simple and flexible architecture based on streaming data flows. It is robust and fault tolerant with tunable reliability mechanisms and many failover and recovery mechanisms.
WHOM WE SERVE
Media & Entertainment