Sugam Sharma

Iowa State University, Center for Survey Statistics and Methodology, Department Member

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Address: Ames, United States

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Emerging Legal Informatics towards Legal Innovation: Current status and future challenges and opportunities Sugam Sharma (Computer Science and Legal Informatics) 1 , Samia Gamoura (AI and Machine Learning) 2 , Deva Prasad (Law) 3 , Arti Aneja (Law) 4

by Sugam Sharma and Samia C H E H B I Gamoura

Legal Information Management, Cambridge University Press, 2021

In the last couple of years, the Legal Informatics area has slowly begun to develop as Artificial... more In the last couple of years, the Legal Informatics area has slowly begun to develop as Artificial Intelligence and its allied techniques and technologies expand their footprints in the field of Law. The legal, computational, and data science communities are collaborating to build the computational and data-driven innovative legal models to improve and advance all aspects of the existing legal system with the effective use of modern computer technologies such as Machine Learning, Deep Learning, and Natural Language Processing.
In this research paper, we have explored the important factors with the potential to transform the existing jurisprudence into a smart and intelligent legal system with a good degree of automation. Shortly, the existence of such a justice system can be envisioned, which is faster, fair, and economically feasible even to the highly marginalized and underprivileged societies globally, “the poor”.

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Towards Querying and Visualization of Large Spatio-Temporal Databases

Dedicated to my parents and my wife Ritu iii TABLE OF CONTENTS LIST OF TABLES …………………………………………………... more Dedicated to my parents and my wife Ritu iii TABLE OF CONTENTS LIST OF TABLES ………………………………………………………………… vi LIST OF FIGURES ………………………………………………………………. vii ACKNOWLEDGMENTS……………………………………………………….. xi ABSTRACT……………………………………………………………………….. xiii CHAPTER 1. INTRODUCTION ……………………………………………….. CHAPTER 2. BACKGROUND AND LITERATURE REVIEW……………… 2.1 Spatio-Temporal Data Models………………………………………………. 2.2 Spatio-Temporal Data Visualization………………………………………... CHAPTER 3. LARGE SPATIO-TEMPORAL DATABASES………………… 3.1 Spatial Databases …………………………………………………………… 3.2 Temporal Databases ………………………………………………………… 3.3 Spatio-Temporal Databases ………………………………………………… CHAPTER 4. PARAMETRIC DATA MODEL ………………………………… 4.1 Overview of Parametric Data Model ………………………………………. 4.2 Concept of Subset in Parametric Data Model ……………………………… 4.3 Concept of Subquery in Parametric Data Model …………………………… 4.4 ParaSQL Extension ………………………………………………………… 4.4.1 Where Clause in ParaSQL ……………………………………………. CHAPTER 5. VISUALIZATION OF LARGE SPATIO-TEMPORAL DATA……………………………………………………………….. 5.1 Need for Visualization of Spatio-temporal Datasets ……………………… 5.2 Visualization in Parametric Data Model …………………………………..

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Classification and Comparison of Leading NoSQL Big Data Models

Today, in data science, the term Big Data has attracted a large set of audience from various dive... more Today, in data science, the term Big Data has attracted a large set of audience from various diversified directly or distantly related domains, in research and industry both, who sniff the potential of Big Data in solving the large and complex problems. In this data science age, the decision making processes are largely data dependent. Though, the concept of Big Data is in the midst of the evolution with great research and business opportunities, the challenges are enormous and growing equally too, starting from data collection up to decision making. This motivates various scientific disciplines to conglomerate their efforts for deep exploration of all dimensions of Big Data to procure evolutionary outcomes. The considerable velocity of the volume expansion and variety of the data pose the serious challenges to the existing data processing systems. Especially, in last few years, the volume of the data has grown manyfold (beyond petabytes). The data storages have been inundated by various disparate potential data outlets, leading by social media such as Facebook, Twitter, etc. The existing data models are largely unable to illuminate the full potential of Big Data; the information that may serve as the key solution to several complex problems is left unexplored. The existing computation capacity falls short for the increasingly expanded storage capacity. The fast-paced volume expansion of the unorganized data entails a complete paradigm shift in new age data computation and witnesses the evolution of new capable data engineering techniques such as capture, curation, visualization, analyses, etc. In this paper, we provide the first level classification for modern Big Data models. Some of the leading NoSQL (largely being translated as “not only SQL”) representatives of each classification that claim to best process the Big Data in reliable and efficient way are also discussed. Also, the classification is further strengthened by the intra-class and inter-class comparisons and discussions of the undertaken NoSQL Big Data models.

Bidirectional migration between variability and commonality in product line engineering of smart homes

The product line engineering (PLE) has proven to be the paradigm for developing diversified produ... more The product line engineering (PLE) has proven to be the paradigm for developing diversified products and systems in shorter time, at lower cost, and higher quality. Ultimately the goal of PLE is to evolve a set of products that have both commonality and variations built into them, which allows a high degree of variability between the different products. The current trend in smart home deployment—based on commissions—guarantees a complete satisfaction for each inhabitant, as designed per resident basis. To reduce high deployment cost and to resolve the non-reusability of the resources, the product line engineering methodology can be employed in selecting or configuring the appropriate smart home technology. Within the smart homes and ambient assisted living, not only do the reusable features reduce cost, they also do not compromise with the guarantee of complete satisfaction of each resident as few senior inhabitants may impose severe constraints on the binding of few common features. In our previous work (Sharma et al., 9th International Conference on Smart Homes and Health Telematics (ICOST), 2011), we propose an approach called, wrenching that claims the best resident satisfaction at the reduced deployment cost. Today seniors are fast growing population globally (http://www.aoa.gov/agingstatsdotnet/Main_Site/Data/2008) and the increased demand for smart homes in the near future is undeniable. Thus to provide assistance and independence to each senior resident of a smart home, reduction in time to market (Wohlin and Ahlgren, Softw Qual J 4:189–205, 1995) of appropriate assistive smart home technology becomes an essential consideration. Long term analysis of variability binding can help to analyze that few variants are bound more often over others. Respecting the satisfaction guarantee, the highly demanded variants can be permanently migrated into commonality, advocating the reusability. The improved reusability of features (e.g., sensors) not only enhances economy of scale but also time to market. The migration of a feature from variability to commonality is known as realizing.

Geo-spatial Pattern Determination for SNAP Eligibility in Iowa Using GIS.

U. S. Department of Agriculture (USDA) administers SNAP - to assist low income citizens at the fe... more U. S. Department of Agriculture (USDA) administers SNAP - to assist low income citizens at the federal level, delegating the benefits distribution to the states. To avail the SNAP benefits the target population should be 50% or more low income (185% of federal poverty level) defined.
This work is intended to examine the spatial pattern of eligibility for SNAP in Iowa using GIS technology. Due to unavailability of income data on individual participant, this research employs the census tracts data as the finest spatial granularity. Using GIS technology, the tracts - meeting the income criteria for SNAP eligibility - are rendered on their county map with distinguish rendition pattern, differentiating them from unqualified ones.

Towards XML Interoperability

Now a day’s distributed computing has become a common phenomenon. As the system designed from the... more Now a day’s distributed computing has become a common phenomenon. As the system designed from the bottom up with networking in mind, distributed computing makes it very easy for computers to cooperate and today, scientific world is nourishing the benefits provided by distributing computing under the broad umbrella of client server architecture. Information is requested and used at various distant physical or logical locations as per the requirement by the users. This is very unlikely that all the engaged users use the same computing environment. XML (Extensive Markup Language) technology has emerged as an efficient medium for information transfer and attempting to offer similar kind of information environment up to an extent by using its potential property called, interoperability. Interoperability is a kind of ability of software and hardware on different machines from different vendors to share data. In this paper we employ a few criteria to evaluate interoperability of XML in heterogeneous distributed computing environment.

Racial Disparity in SNAP? No: A Geospatial Study for Iowa

To assist the low or no-income population, USDA governs an aid programme, called SNAP. As the spa... more To assist the low or no-income population, USDA governs an aid programme, called SNAP. As the spatial granularity changes from national to state, the SNAP distribution responsibilities delegate to states. The federal poverty level (185% or less) and 50% or more low income populous at a tract level defines the target households for SNAP. Lately, there is a widespread stereotype that SNAP (food stamp) eligibility has racial disparity. A remark from a US republican presidential candidate on February 25th, 2012 also links the food stamp to racial disparity. This work is intended to examine the hypothesis that in the USA SNAP has racial disparity. Iowa is employed as the region of study and the census tracts data is considered as finest spatial granularity as income data for individual participant is unavailable. GIS technology helps in differentiating SNAP eligible tracts from unqualified ones and displays the racial/ethnic diversity.

Parametric database approach integration for handling temporal data in GIS

In large spatial, temporal, and spatio-temporal databases, the concept of dimensions – time and s... more In large spatial, temporal, and spatio-temporal databases, the concept of dimensions – time and space – is central and their efficient processing has always been a great concern. A Geographic Information System (GIS) is widely known for its efficient and sophisticated handling of the spatial databases; however, integration of the time domain introduces additional complexities. Considerable research is ongoing in this area. Though numerous approaches have already been proposed in the literature to address the time-related challenges in GIS community, such as the development of temporal GIS data models, this study contributes to these ongoing efforts through extensions to the Parametric Data Model. Parametric database approaches (Parametric Data Model) have been developed to handle dimensional data such as space, time, belief, etc. In this study, the time dimension is of specific interest and we propose the integration of parametric approaches to GIS to absorb the temporal burden. The temporal processing of the data is done using parametric approaches that address the limitation of GIS to support temporal queries and temporal analysis of phenomena. Using the AutoConViz tool, developed for spatio-temporal data modeling and visualization, results can be directly integrated into standard GIS software programs such as ArcGIS and further processed seamlessly along with and in a similar manner to other attributes.

SubVizCon: Subsetting through Visualization and Conversion in GIS to Assist Complex Queries on Large Spatio-Temporal Datasets.

SubVizCon: Subsetting through Visualization and Conversion in GIS to Assist Complex Queries on La... more

A BRIEF REVIEW ON LEADING BIG DATA MODELS

Today, science is passing through an era of transformation, where the inundation of data, dubbed ... more Today, science is passing through an era of transformation, where the inundation of data, dubbed data deluge is influencing the decision making process. The science is driven by the data and is being termed as data science. In this internet age, the volume of the data as grown up to petabytes, and this large, complex, structured or unstructured, and heterogeneous data in the form of “Big Data” has gained significant attention. The rapid pace of data growth through various disparate sources, especially social media such as Facebook, has seriously challenged the data analytic capabilities of traditional relational databases. The velocity of the expansion of the amount of data gives rise to a complete paradigm shift in how new age data is processed. Confidence in the data engineering of the existing data processing systems is gradually fading whereas the capabilities of the new techniques for capturing, storing, visualizing, and analyzing data are evolving. In this review paper, we discuss some of the modern Big Data models that are leading contributors in the NoSQL era and claim to address Big Data challenges in reliable and efficient ways. Also, we take the potential of Big Data into consideration and try to reshape the original operational oriented definition of “Big Science” (Furner, 2003) into a new data-driven definition and rephrase it as “The science that deals with Big Data is Big Science.”

Leading NoSQL Models for Handling Big Data: A Brief Review

Today, in data science, the large, complex, structured or unstructured, and heterogeneous data ha... more Today, in data science, the large, complex, structured or
unstructured, and heterogeneous data has gained significant attention. The data is being generated at a very rapid pace through various disparate potential resources and sensors, scientific instruments, and internet, especially the social
media, are just to name a few. The considerable velocity of the volume expansion of the data possesses the serious challenges for the existing data
processing systems. In this paper, we review some of the modern data models that claim to process the extremely large data such as Big Data (of petabyte size) in reliable and efficient way and are the leading contributors in NoSQL (largely being translated as ‘not only SQL’) era.

XCloudX always Implies Cloud Computing in Big Data? A Brief Investigation

Today, a paradigm shift is being observed in science, where the focus is gradually shifting away ... more Today, a paradigm shift is being observed in science, where the focus is gradually shifting away from operation to data; which is greatly influencing the decision making also. The data is being inundated proactively from several sources in various forms; especially social media such as Facebook and in modern data science vocabulary is being recognized as Big Data. Today, Big Data is permeating through the bigger aspect of human life for scientific and commercial dependencies, especially for massive scale data analytics of beyond the exabyte magnitude. As the footprint of Big Data applications such as social mining, data simulation, etc. is regularly expanding, the reliability on cloud environments is also increasing to obtain appropriate services to deal with Big Data challenges. Several data models to efficiently process Big Data are already developed and a number of such models are still emerging, relying on heterogeneous underlying storage technologies, including cloud. In this paper, we zoom in to gauge the intuitiveness of the name of the cloud-assisted NoSQL Big Data models and investigate whether XCloudX always use cloud computing environment underneath or vice versa. XCloudX symbolizes those NoSQL Big Data models that embody the term “cloud” in their name.

Nature Findings on Big Data

Contextual Motivation in Physical Activity by Means of Association Rule Mining

The primary thrust of this work is to demonstrate the applicability of association rule mining in... more The primary thrust of this work is to demonstrate the applicability of association rule mining in public health domain, focusing on physical activity and exercising. In this paper, the concept of association rule mining is shown assisting to promote the physical exercise as regular human activity. Specifically, similar to the prototypical example of association rule mining, market basket analysis, our proposed novel approach considers two events - exercise (sporadic) and sleep (regular) as the two items of the frequent set; and associating the former, exercise event, with latter, the daily occurring activity sleep at night, helps strengthening the frequency of the exercise patterns. The regularity can further be enhanced, if the exercising instruments are kept in the vicinity of the bed and are within easy reach.

AutoConViz: automating the conversion and visualization of spatio-temporal query results in GIS

Geo-spatial Information Science, 2012

In recent years, large multifaceted spatial, temporal, and spatio-temporal databases have attaine... more In recent years, large multifaceted spatial, temporal, and spatio-temporal databases have attained significant popularity and importance in the database community. In order to perform preliminary investigation, exploratory visual analysis of such data-sets is highly desirable. To facilitate the convenient and efficient visualization, scientists and practitioners often need to convert the spatial component of the data-set into a more usable format. Though among the various formats available today in spatial data science community, Geographical Markup Language (GML) adheres to its central position and is of our interest in this work. The development of a tool to satisfy the spatial format conversion needs tailored to every user’s needs from scratch is difficult, time-consuming, and requires skills not easy to possess. We developed AutoConViz, to solve the issue stated above. It accepts the spatial component in GML format, converts that into shapefile format, and facilitates informative and automated interaction with the data-sets. It supports basic query and geospatial analysis and visualization tasks and offers functionalities such as zooming, panning, and feature selection. Furthermore, our software leverages navigation to classical ArcGIS software interface for users interested in more intensive analysis. AutoConViz serves both the database and geographical information system communities to explore insights of spatio-temporal databases and will help to further geospatial research and development.

On Analyzing the Degree of Coldness in Iowa, a North Central Region, United States: An XML Exploitation in Spatial Databases

State of Iowa is an agricultural rich state in north central region and is divided into 99 counti... more State of Iowa is an agricultural rich state in north central region and is divided into 99 counties. NCRA in the United States maintains agricultural databases to facilitate crop and risk analysis, pest management and forecasting. NC94 is one such dataset which is intensively used and is available for public use through many sources to process and analyze to get future predictions about agriculture. In this work we calculate the cumulative degree of coldness in Iowa with spatial granularity as county in last 30 years. To demonstrate the degree of coldness, we choose blue as the base color and counties are rendered with different shades of blue color based on the degree of coldness. Higher intensity of the color reflects the higher coldness whereas the lower intensity corresponds to lower coldness. We expect that the results of this research provide direct benefits to farmers and will attract the attention of agricultural/ computational scientific community.

Four-Layer Architecture Model for Energy Conservation in Wireless Sensor Networks

We propose a four-layer architecture model (FLAM) to obtain the potential energy savings for sens... more We propose a four-layer architecture model (FLAM) to obtain the potential energy savings for sensor nodes in large scale wireless sensor networks. The very first layer, the bottom layer is a network of a resource-constrained sensor nodes deployed in the field. The second layer is ad hoc network formed by resource-rich dynamic sink nodes, also knows as cluster sink nodes. The third layer consists of resource-rich static sink nodes, known as zonal sink also, responsible for data collection from cluster sinks of its zone. The fourth (top) layer posses a resource-rich flying sink. We call this sink as hawk sink and is responsible for data collection from zonal sinks. A cluster sink may be few hops away from a static sensor node and moves to the vicinity of sensor nodes to collect the data and continuously updates its zonal sink by propagating the collected data from the respective cluster. Each zonal sink buffers the collected data and propagates to hawk sink through query response mechanism when later enters the transmission range of the zonal sink. Hawk sink is the one, responsible for data transmission from network environment to the base station. FLAM approach is scalable, increases sensor life by allowing them to operate with a limited transmission range only and provides reduced delay as cluster sink continuously updates to zonal sink without query.

On the Calculation of Coldness in Iowa, a North Central Region, United States: A Summary on XML Based Scheme

NC94 dataset is an agricultural dataset which consists of climate, crop, and soil data and is con... more NC94 dataset is an agricultural dataset which consists of climate, crop, and soil data and is considered as a completely dataset available. The data is organized based on county and is treated as an object. It is adapted in interdisciplinary research and widely used in various research domains and once again has been exploited in our parametric database research. In this research work, we exploit the climate data of NC94 dataset to calculate the cumulative degree of coldness in Iowa in last 30 years. To demonstrate the degree of coldness, an xml based scheme is used considering blue as the base color with the notion that darker shade reflects higher degree of coldness. This work is a baby step towards the generation of climate atlas of North Central Region of USA in one single click.

An XML-based range variation approach to render the coldness in Iowa, a North Central region, United States

State of Iowa, in the North Central region of United State of America is an agricultural rich sta... more State of Iowa, in the North Central region of United State of America is an agricultural rich state. It is also known as the "Food Capital of the World". NC-94 is an agricultural database of north central region in United States that facilitates crop and risk analysis, pest management and forecasting and used intensively. It is available for public use through many sources to process and analyze to get future predictions about agriculture. In this research work we exploit the use of XML as configuration support also to generate different sets of shades with blue as base color and use those sets to render the degree of coldness correspondingly in Iowa with county as spatial granularity in last 30 years. The darker and lighter shades reflect the higher and lower degree of coldness respectively.

Three-layer architecture model (TLAM) for energy conservation in wireless sensor networks

We propose a three-layer architecture model (TLAM) for wireless sensor networks. The first layer ... more We propose a three-layer architecture model (TLAM) for wireless sensor networks. The first layer (bottom layer) consists of a network of resource-constrained sensor nodes deployed in the field. The second layer contains a set of resource-rich dynamic sink nodes forming an ad hoc network. The sinks at layer second are responsible to monitor and collect data from a region, called cluster. So the sinks at second layer are also knows as cluster sinks. At the third layer, there is a resource-rich static sink, known as zonal sink also, responsible for data collection from cluster sinks. A cluster sink may be few hops away from a static sensor node and moves to the vicinity of sensor nodes to collect the data. Each cluster sink continuously updates the zonal sink by propagating the collected data from the respective cluster. The zonal sink buffers the collected data and propagates to base station. Zonal sink is the one, responsible for data transmission from network environment to the base station, where the data is processed for further use. TLAM approach is robust, increases sensor life by allowing them to operate with a limited transmission range only and provides reduced delay as cluster sink continuously updates to zonal sink without query.

Sugam Sharma

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