# Top PDF Think Data Structures: Algorithms and Information Retrieval in Java

### Think Data Structures: Algorithms and Information Retrieval in Java

In this method, the loop runs once for each element in collection. If collection contains m elements and the list we are removing from contains n elements, this method is in O(nm). If the size of collection can be considered con- stant, removeAll is linear with respect to n. But if the size of the collection is proportional to n, removeAll is quadratic. For example, if collection al- ways contains 100 or fewer elements, removeAll is linear. But if collection generally contains 1% of the elements in the list, removeAll is quadratic. When we talk about problem size, we have to be careful about which size, or sizes, we are talking about. This example demonstrates a pitfall of algorithm analysis: the tempting shortcut of counting loops. If there is one loop, the algorithm is often linear. If there are two loops (one nested inside the other), the algorithm is often quadratic. But be careful! You have to think about how many times each loop runs. If the number of iterations is proportional to n for all loops, you can get away with just counting the loops. But if, as in this example, the number of iterations is not always proportional to n, you have to give it more thought.

### Evolutionary algorithms and machine learning techniques for information retrieval

In this study, MAP, NDCG@10, P@10, RR@10 and RMSE were used (Baeza-Yates and Ribeiro-Neto, 2011; Li, 2014) as five separate fitness functions on the training sets. They also were used as the evaluation metrics for the ranking functions on the test sets. The variants of the proposed LTR method are called ES-Rank (baseline initialisation), IESR-Rank (linear regression initialisation) and IESVM-Rank (support vector machine initialisation). Tables 7.2, 7.3, 7.4, 7.5 and 7.6 show the overall results for all the methods tested. The other fourteen methods are implemented in the packages RankLib (Dang, 2016), Sofia-ml (Sculley, 2010), SVMRank (Joachims, 2016), Layered Genetic Programming for LTR (RankGP) (Lin et al., 2007b; Mick, 2016) and rt-rank for IGBRT (Mohan et al., 2011). IGBRT technique has not MAP, P@10 and RR@10 results due to the limitation of rt-rank package. The parameter values used for those other approaches are the default settings in these packages. Those settings produced the shortest computational run time and the lowest memory size requirements for each approach. The experimental results presented are the average scores of five runs on 5-folds cross validation. Each dataset fold consists of a training, a validation and a testing data. Experiments were conducted on a PC with 3.60 GHz Intel (R) core(TM) i7-3820 CPU and 8GB RAM. The implementation was in Java NetBeans under Windows 7 Enterprise Edition.

### Data Structures and Algorithms in Java, 5th Edition pdf

As we mentioned in Section 1 .5, an array reference in Java points to an array object. Thus, if we have a two-dimensional array, A, and another two-dimensional array, B, that has the same entries as A, we probably want to think that A is equal to B. But the one-dimensional arrays that make up the rows of A and B are stored in different memory locations, even though they have the same intemal content. Therefore, a eaU to the method java.utii.Arrays.equals(A,B) will return false in this case. The reason for this behavior is that this equals() method tests for shallow equality, that is, it tests only whether the corresponding elements in A and B are equal to each other using only a simple notion of equality. This simple equality rule says that two base type variables are equal if they have the same value and two object references are equal if they both refer to the same object. Fortunately, if we want to have a deep equality test for arrays of objects, like two-dimensional arrays, the java .utii.Arrays class provides the following method:

### Data Structures and Algorithms in Java, 4th Edition pdf

objectoriented viewpoint throughout this text. One of the main ideas of the object- oriented approach is that data should be presented as being encapsulated with the methods that access and modify them. That is, rather than simply viewing data as a collection of bytes and addresses, we think of data as instances of an abstract data type (ADT) that include a repertory of methods for performing operations on the data. Likewise, object-oriented solutions are often organized utilizing common design patterns, which facilitate software reuse and robustness. Thus, we present each data structure using ADTs and their respective implementations and we introduce important design patterns as means to organize those implementations into classes, methods, and objects.

### Data Structures and Algorithms Alfred V Aho pdf

Almost any branch of mathematics or science can be called into service to help model some problem domain. Problems essentially numerical in nature can be modeled by such common mathematical concepts as simultaneous linear equations (e.g., finding currents in electrical circuits, or finding stresses in frames made of connected beams) or differential equations (e.g., predicting population growth or the rate at which chemicals will react). Symbol and text processing problems can be modeled by character strings and formal grammars. Problems of this nature include compilation (the translation of programs written in a programming language into machine language) and information retrieval tasks such as recognizing particular words in lists of titles owned by a library.

### Information Retrieval Data Structures And Algorithms FRAKES WB (2004) pdf

I exaggerated, of course, when I said that we are still using ancient technology for information retrieval. The basic concept of indexes--searching by keywords--may be the same, but the implementation is a world apart from the Sumerian clay tablets. And information retrieval of today, aided by computers, is not limited to search by keywords. Numerous techniques have been developed in the last 30 years, many of which are described in this book. There are efficient data structures to store indexes, sophisticated query algorithms to search quickly, data compression methods, and special hardware, to name just a few areas of extraordinary advances. Considerable progress has been made for even seemingly elementary problems, such as how to find a given pattern in a large text with or without preprocessing the text. Although most people do not yet enjoy the power of computerized search, and those who do cry for better and more powerful methods, we expect major changes in the next 10 years or even sooner. The wonderful mix of issues presented in this collection, from theory to practice, from software to hardware, is sure to be of great help to anyone with interest in information retrieval.

### A generic framework for ontology-based information retrieval and image retrieval in web data

For the past two decades, a considerable amount of research has been performed in Image Retrieval (IR). In traditional text-based image annotations, the images are manu- ally annotated by humans, and the annotations are used as an index for image retrieval [13, 14]. The second well-known approach in image retrieval is Content-Based Image Retrieval (CBIR) where the low-level image features, such as color, texture and shape are used as an index for image retrieval [15–17]. The third approach is Automatic Image Annotation (AIA) where the system learns semantic information from image concepts and uses that knowledge to label a new image [18, 19]. There are some benchmark image datasets available, such as IAPR TC-12, that have proper image content descriptions. ImageCLEF [20] can be used for ad-hoc image retrieval tasks via text and/or content- based image retrieval of CLEF from 2006 onwards [21]. To query results from ontology, SPARQL [22, 23] is used as the query language using Jena Fuseki [24], which is a server that stores all RDFs. However, the image retrieval result is accurate if the annotations are perfect.

### How to Think Like a Computer Scientist. Java Version

Some lists are “well-formed;” others are not. For example, if a list contains a loop, it will cause many of our methods to crash, so we might want to require that lists contain no loops. Another requirement is that the length value in the IntList object should be equal to the actual number of nodes in the list. Requirements like this are called invariants because, ideally, they should be true of every object all the time. Specifying invariants for objects is a useful programming practice because it makes it easier to prove the correctness of code, check the integrity of data structures, and detect errors.

### Algorithms and Dynamic Data Structures for Basic Graph Optimization Problems.

Our data structure of dual-failure distance queries is much more complicated than those of [16, 5], which can be seen from the number of cases caused by the second failed vertex/edge. If p is a shortest path from x to y and u a failed vertex, the shortest path avoiding u consists of a prefix of the original path p followed by a “detour” avoiding p (and u), followed by a suffix of p. In the presence of 2 failures (assumed to be u and v) it is no longer possible to create such a clean partition. The shortest path avoiding two failed vertices on p may depart from and return to p many times, because p can be directed and the detour can travel back on p. When we first find the detour only avoiding u, then v may be on this detour, but if we further find the detour avoiding v, the new detour may still pass through u. So we will need much more complex structures and query algorithm to deal with this. With 3 (or more) failures the possible cases of the optimal detours becomes even more complicated. The conclusion we draw from our results is that handling dual-failure distance queries is possible but extending our structure to handle 3 or more failures is practically infeasible.

### Data structures and algorithms for manipulation and display in computer simulated surgery

In some cases however it is definitely inappropriate to use the j B-R approach. For example in the complex branching structure of the facial skeleton, or in the visualisation of vascular beds [PARKER 86 ] where decisions about directions of branching are almost impossible to make automatically without a very sophisticated model-based approach. For these reasons, among others, the Spatial Enumerative (voxel) approaches were developed, originally by MIPG in what Herman called the Cuberille Environment (a binary digital array) [HERMAN77, 79]. Because this group’s original interest was in the display of surfaces in reasonable times on small computers, the first approaches in the Cuberille Environment were also surface based. These used an elegant and ingenious algorithm for extracting lists of faces only of those voxels that were on the surface, in the sense that they had no immediate neighbour in the direction of view [ARTZY81, HERMAN83b]. These programs allowed the display of much more complex structures, and had the advantage of being "unambiguous" in the sense that they only displayed data that represented the presence or absence of the object within a voxel. In fact there still existed some algorithm-dependent structure; in particular the method of defining the object which could depend on the threshold chosen or on the behaviour of some surface tracking or region growing algorithm. Also, the necessary interpolation step which compensates for the non-cubic nature of the original voxel data due to undersampling in the coaxial direction can be implemented in several ways. The MIPG methods are discussed in more detail in sections 2.3.2.1 (segmentation), 3.1.2 (representation) and 4.4.1.2.2 (display).

### Comparing Performance of Various Optimization Algorithms for Effective Information Retrieval – A Review

During the last decade the information over the web have increased and optimization of information retrieval effectiveness has driven the quality of the results over the web, People are more trusting and preferring web search as a source of information. Information retrieval has come out of academic discipline to become the basis of most preferred and reliable source of information. The field of information retrieval began with scientific library records and scientific publications; it spread rapidly in other domains like journalism, lawyers and medical fields. Information retrieval then spread in web information access. The information retrieval provides solution in finding relevant information in unstructured information [5].

### Prediction and Retrieval of Information in Big Data Technology with Data Warehouse

The mongo database model consist of group of land records. The model of the mongo database is a record database model. With mongo database, the organizations can address diverse application needs, hardware resources, and deployment designs with a single database technology. Mongo DB can be extended with new capabilities, and configured for optimal use of specific hardware architectures. This approach significantly reduces developer and operational complexity compared to running multiple databases to power applications with unique requirements. Users can control the same mongo DB query language, data model, scaling, security and operational tooling across different applications, each powered by different pluggable mongo DB storage engines. Besides, it provide better scalability for storing all the classified spatial, textual, and the transaction details of the land record information in a single database.

### Object Oriented Data Structures Using Java Nell Dale pdf

We have just looked at various ways of combining Java’s built-in type mechanisms to create composite objects, arrays of objects, and two-dimensional arrays. We do not have to stop there. We can continue along these lines to create whatever sort of structure best matches our data. Classes can have arrays as variables, aggregate objects can be made from other aggregate objects, and we can create arrays of three, four, or more dimensions. Consider, for example, how a programmer might structure data that represents stu- dents for a professor’s grading program. This professor grades each test with both a numerical grade and a letter grade. Therefore, the programmer decides to represent a test as a record, called test , with two fields: score of type int and grade of type char . Each student takes a sequence of tests—these are represented by an array of test called marks . A student also has a name and an attendance record. So a student

### Augmenting Data Retrieval with Information Retrieval Techniques by Using Word Similarity

Section 3.2, function most efficiently by using reduced tables of two different sizes. We chose two threshold values for these reduced tables to optimize execution time without significantly affecting the accuracy of data-record retrieval. Ideal threshold values have been determined experimentally to be 3 × 10 −6 and 5 × 10 −7 , which yield two tables containing 0.18% and 1.3% of the word-similarity values contained in the 13%- W ST , referred as the 10 −6 -13%- W ST and 10 −7 -13%- W ST , respectively. The size of each similarity matrix and table is displayed in Figure 1(b) and Table 1. Before we can apply WSM to search documents using the 10 −6 - and 10 −7 -13%- W ST , data records and their corresponding schemes in an RDBMS must first be extracted from the source data. Document Representation We use “document” to represent a Web page, which can be an academic paper, a sports news article, etc. We assume that each document is represented by the (non-stop, stemmed) keywords in its “abstract,” which can be a title, a brief description, or a short summary of the content of the respective document. Before we search the abstract of a document, it must first be formatted as a data record with non-repeated, non-stop, stemmed keywords in the KeywordAbstract table in where each tuple < Keyword, Doc-ID > contains a keyword and a reference to the (abstract of the) corresponding document record in which it is contained. This essentially creates a word index for searching the occurrences of keywords among all the abstracts quickly.

### Efficient Retrieval of Information using Talend Data Integration

necessary to capture and monitor the development of an individual. This paper describes the methodology of data warehouse used for analysis, generating reports and related tools for support of those technologies, which are used to generate reports. Employees and other stakeholders needs information about insight into the existing data, so as to analyze and retrieve data in an efficient manner without disturbing the daily activity of an Online Transaction Processing (OLTP) system. This is a complex problem during the decision-making process. To solve this problem, the information about the employee and other stakeholders are stored at a structured format in the data warehouse and the report is generated respectively.

### Algorithms and Data Structures for Sequence Analysis in the Pan-Genomic Era

Since it became evident, in the 80s [17], that indexed string matching is relevant for analysing biological sequences, bioinformatics has played an important role in pushing the development of the field. To increase applica- bility of index data structures there has been a continuous effort to reduce their size while retaining strong search capability. A classic example is the Suffix Array [65], which uses much less space than the Suffix Tree [96], and can provide the same functionality when augmented appropriately [1]. This trend has led to the development of a whole research area, compressed in- dexing [72], where the idea is to build an index that uses space proportional to a compressed representation of the text. Particularly important results, like the FM-Index [30, 31], are based on the Burrows-Wheeler transform (BWT) [12]. Those indexes are currently at the heart of widely-used read aligners such as BWA [60] and BowTie2 [57].

### Data Fusion in Patient Centered Health Information Retrieval

CLEF_Run: In order to answer research question one (RQ1), we used the PL2 term weighting model in the Terrier-4.0 IR platform. As improvement, we deployed the collection enrichment approach [18], where we selected the expansion terms from an external collection, which was made up of a collection of documents from CLEF_2015_eHealth. We used the Terrier-4.0 Divergence from Randomness (DFR) Bose – Einstein 1 (Bo1) model for query expansion to select the 10 most informative terms from the top 3 ranked documents after the first pass retrieval (on the external collection). We then performed a second pass retrieval on the local collection (ClueWeb12-B13) with the new expanded query. In Table 3, we present a selection of expanded, preprocessed test queries (stemmed and tokenized). These queries have been assigned term weights by the Bose – Einstein 1 (Bo1) model for query expansion.

### Efficient Clustering Technique for Information Retrieval in Data Mining

459 The activity of exploring a collection of documents takes place when there is no information need or it is too vague to formulate a specific query. For example, imagine creating a query for the following question: given a set of previously unseen documents, what subjects are they about? An alternative task could be this: what subjects dominate the headlines of all major newspapers today? A human being could answer these questions simply by reading through all the available documents, but such solution is usually unacceptable as it requires too much time and effort. Exploration problems are also encountered in combination with search engines. Queries issued to search engines are mostly short and ambiguous and match vast numbers of documents concerning variety of subjects. Creating a linear hit list out of such a broad set of results often requires trade-offs and hiding documents that could prove useful to the user. If shown an explicit structure of topics present in a search result, users quickly narrow the focus to just a particular subset (slice) of all returned documents [2] and [6].

### Neural Models for Information Retrieval without Labeled Data

I had the honor of collaborating with over 50 researchers so far, as either mentor, mentee, or collaborator. I have not only learned a lot from them, but also made many great friends. In particular, I would like to thank my Master’s advisor, Azadeh Shakery, for introducing me to the field of information retrieval. I would also like to thank my internship mentors and collaborators at Google Research and Microsoft Research for giving me the opportunity to see what it was like to be a researcher in industrial research labs. They include Michael Bendersky, Paul Bennett, Nick Craswell, Susan Dumais, Gord Lueck, Bhaskar Mitra, Xia Song, Saurabh Tiwary, Xuanhui Wang, and Mingyang Zhang. I must also acknowledge the help and support of Nick Craswell, Mostafa Dehghani, Fernando Diaz, and Hang Li while organizing the first international workshop on learning from limited or noisy data for information retrieval (LND4IR) at SIGIR 2018. A special thanks to Mohammad Aliannejadi, Mostafa Dehghani, Jaap Kamps, and Markus Schedl for the productive collaborations we had throughout the last few years.